diff --git a/contents/ai_for_good/ai_for_good.bib b/contents/ai_for_good/ai_for_good.bib index bc2d5d4a..f50928c1 100644 --- a/contents/ai_for_good/ai_for_good.bib +++ b/contents/ai_for_good/ai_for_good.bib @@ -1,3 +1,6 @@ +%comment{This file was created with betterbib v5.0.11.} + + @inproceedings{altayeb2022classifying, author = {Altayeb, Moez and Zennaro, Marco and Rovai, Marcelo}, booktitle = {Proceedings of the 2022 ACM Conference on Information Technology for Social Good}, @@ -7,7 +10,8 @@ @inproceedings{altayeb2022classifying source = {Crossref}, title = {Classifying mosquito wingbeat sound using {TinyML}}, url = {https://doi.org/10.1145/3524458.3547258}, - year = {2022} + year = {2022}, + month = sep, } @inproceedings{bamoumen2022tinyml, @@ -20,7 +24,8 @@ @inproceedings{bamoumen2022tinyml source = {Crossref}, title = {How {TinyML} Can be Leveraged to Solve Environmental Problems: {A} Survey}, url = {https://doi.org/10.1109/3ict56508.2022.9990661}, - year = {2022} + year = {2022}, + month = nov, } @article{duisterhof2019learning, @@ -29,7 +34,7 @@ @article{duisterhof2019learning title = {Learning to seek: {Autonomous} source seeking with deep reinforcement learning onboard a nano drone microcontroller}, url = {https://arxiv.org/abs/1909.11236}, volume = {abs/1909.11236}, - year = {2019} + year = {2019}, } @inproceedings{duisterhof2021sniffy, @@ -42,7 +47,8 @@ @inproceedings{duisterhof2021sniffy source = {Crossref}, title = {Sniffy Bug: {A} Fully Autonomous Swarm of Gas-Seeking Nano Quadcopters in Cluttered Environments}, url = {https://doi.org/10.1109/iros51168.2021.9636217}, - year = {2021} + year = {2021}, + month = sep, } @article{jia2023life, @@ -57,7 +63,8 @@ @article{jia2023life title = {Life-threatening ventricular arrhythmia detection challenge in implantable cardioverter{\textendash}defibrillators}, url = {https://doi.org/10.1038/s42256-023-00659-9}, volume = {5}, - year = {2023} + year = {2023}, + month = may, } @inproceedings{ooko2021tinyml, @@ -70,7 +77,8 @@ @inproceedings{ooko2021tinyml source = {Crossref}, title = {{TinyML} in Africa: {Opportunities} and Challenges}, url = {https://doi.org/10.1109/gcwkshps52748.2021.9682107}, - year = {2021} + year = {2021}, + month = dec, } @article{ramcharan2017deep, @@ -84,15 +92,16 @@ @article{ramcharan2017deep title = {Deep Learning for Image-Based Cassava Disease Detection}, url = {https://doi.org/10.3389/fpls.2017.01852}, volume = {8}, - year = {2017} + year = {2017}, + month = oct, } @misc{rao2021, author = {Rao, Ravi}, journal = {www.wevolver.com}, - month = {Dec}, + month = dec, url = {https://www.wevolver.com/article/tinyml-unlocks-new-possibilities-for-sustainable-development-technologies}, - year = {2021} + year = {2021}, } @article{seyedzadeh2018machine, @@ -107,7 +116,8 @@ @article{seyedzadeh2018machine title = {Machine learning for estimation of building energy consumption and performance: {A} review}, url = {https://doi.org/10.1186/s40327-018-0064-7}, volume = {6}, - year = {2018} + year = {2018}, + month = oct, } @article{tirtalistyani2022indonesia, @@ -122,13 +132,14 @@ @article{tirtalistyani2022indonesia title = {{Indonesia} Rice Irrigation System: {Time} for Innovation}, url = {https://doi.org/10.3390/su141912477}, volume = {14}, - year = {2022} + year = {2022}, + month = sep, } @misc{vectorborne, howpublished = {https://www.who.int/news-room/fact-sheets/detail/vector-borne-diseases}, note = {(Accessed on 10/17/2023)}, - title = {Vector-borne diseases} + title = {Vector-borne diseases}, } @misc{verma2022elephant, @@ -137,7 +148,7 @@ @misc{verma2022elephant journal = {Hackster.io}, title = {Elephant {AI}}, url = {https://www.hackster.io/dual\_boot/elephant-ai-ba71e9}, - year = {2022} + year = {2022}, } @article{vinuesa2020role, @@ -152,7 +163,8 @@ @article{vinuesa2020role title = {The role of artificial intelligence in achieving the Sustainable Development Goals}, url = {https://doi.org/10.1038/s41467-019-14108-y}, volume = {11}, - year = {2020} + year = {2020}, + month = jan, } @inproceedings{zennaro2022tinyml, @@ -160,5 +172,5 @@ @inproceedings{zennaro2022tinyml booktitle = {The UN 7th Multi-stakeholder Forum on Science, Technology and Innovation for the Sustainable Development Goals}, pages = {2022--05}, title = {{TinyML:} {Applied} {AI} for development}, - year = {2022} + year = {2022}, } diff --git a/contents/benchmarking/benchmarking.bib b/contents/benchmarking/benchmarking.bib index 2866fe58..c0f9dce1 100644 --- a/contents/benchmarking/benchmarking.bib +++ b/contents/benchmarking/benchmarking.bib @@ -1,3 +1,6 @@ +%comment{This file was created with betterbib v5.0.11.} + + @inproceedings{adolf2016fathom, author = {Adolf, Robert and Rama, Saketh and Reagen, Brandon and Wei, Gu-yeon and Brooks, David}, booktitle = {2016 IEEE International Symposium on Workload Characterization (IISWC)}, @@ -8,21 +11,24 @@ @inproceedings{adolf2016fathom source = {Crossref}, title = {Fathom: {Reference} workloads for modern deep learning methods}, url = {https://doi.org/10.1109/iiswc.2016.7581275}, - year = {2016} + year = {2016}, + month = sep, } @inproceedings{antol2015vqa, - author = {Stanislaw Antol and Aishwarya Agrawal and Jiasen Lu and Margaret Mitchell and Dhruv Batra and C. Lawrence Zitnick and Devi Parikh}, + author = {Antol, Stanislaw and Agrawal, Aishwarya and Lu, Jiasen and Mitchell, Margaret and Batra, Dhruv and Zitnick, C. Lawrence and Parikh, Devi}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/iccv/AntolALMBZP15.bib}, - booktitle = {2015 {IEEE} International Conference on Computer Vision, {ICCV} 2015, Santiago, Chile, December 7-13, 2015}, - doi = {10.1109/ICCV.2015.279}, + booktitle = {2015 IEEE International Conference on Computer Vision (ICCV)}, + doi = {10.1109/iccv.2015.279}, pages = {2425--2433}, - publisher = {{IEEE} Computer Society}, + publisher = {IEEE}, timestamp = {Wed, 24 May 2017 01:00:00 +0200}, - title = {{VQA:} Visual Question Answering}, - url = {https://doi.org/10.1109/ICCV.2015.279}, - year = {2015} + title = {{VQA:} {Visual} Question Answering}, + url = {https://doi.org/10.1109/iccv.2015.279}, + year = {2015}, + source = {Crossref}, + month = dec, } @article{banbury2020benchmarking, @@ -31,7 +37,7 @@ @article{banbury2020benchmarking title = {Benchmarking tinyml systems: {Challenges} and direction}, url = {https://arxiv.org/abs/2003.04821}, volume = {abs/2003.04821}, - year = {2020} + year = {2020}, } @article{beyer2020we, @@ -40,33 +46,35 @@ @article{beyer2020we title = {Are we done with imagenet?}, url = {https://arxiv.org/abs/2006.07159}, volume = {abs/2006.07159}, - year = {2020} + year = {2020}, } @inproceedings{brown2020language, - author = {Tom B. Brown and Benjamin Mann and Nick Ryder and Melanie Subbiah and Jared Kaplan and Prafulla Dhariwal and Arvind Neelakantan and Pranav Shyam and Girish Sastry and Amanda Askell and Sandhini Agarwal and Ariel Herbert{-}Voss and Gretchen Krueger and Tom Henighan and Rewon Child and Aditya Ramesh and Daniel M. Ziegler and Jeffrey Wu and Clemens Winter and Christopher Hesse and Mark Chen and Eric Sigler and Mateusz Litwin and Scott Gray and Benjamin Chess and Jack Clark and Christopher Berner and Sam McCandlish and Alec Radford and Ilya Sutskever and Dario Amodei}, + author = {Brown, Tom B. and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel M. and Wu, Jeffrey and Winter, Clemens and Hesse, Christopher and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario}, + editor = {Larochelle, Hugo and Ranzato, Marc'Aurelio and Hadsell, Raia and Balcan, Maria-Florina and Lin, Hsuan-Tien}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/BrownMRSKDNSSAA20.bib}, booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual}, - editor = {Hugo Larochelle and Marc'Aurelio Ranzato and Raia Hadsell and Maria{-}Florina Balcan and Hsuan{-}Tien Lin}, timestamp = {Tue, 19 Jan 2021 00:00:00 +0100}, title = {Language Models are Few-Shot Learners}, url = {https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html}, - year = {2020} + year = {2020}, } @inproceedings{chu2021discovering, - author = {Grace Chu and Okan Arikan and Gabriel Bender and Weijun Wang and Achille Brighton and Pieter{-}Jan Kindermans and Hanxiao Liu and Berkin Akin and Suyog Gupta and Andrew Howard}, + author = {Chu, Grace and Arikan, Okan and Bender, Gabriel and Wang, Weijun and Brighton, Achille and Kindermans, Pieter-Jan and Liu, Hanxiao and Akin, Berkin and Gupta, Suyog and Howard, Andrew}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/cvpr/ChuABWBKLAG021.bib}, - booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition Workshops, {CVPR} Workshops 2021, virtual, June 19-25, 2021}, - doi = {10.1109/CVPRW53098.2021.00337}, + booktitle = {2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, + doi = {10.1109/cvprw53098.2021.00337}, pages = {3022--3031}, - publisher = {Computer Vision Foundation / {IEEE}}, + publisher = {IEEE}, timestamp = {Mon, 18 Jul 2022 01:00:00 +0200}, title = {Discovering Multi-Hardware Mobile Models via Architecture Search}, - url = {https://openaccess.thecvf.com/content/CVPR2021W/ECV/html/Chu\_Discovering\_Multi-Hardware\_Mobile\_Models\_via\_Architecture\_Search\_CVPRW\_2021\_paper.html}, - year = {2021} + url = {https://doi.org/10.1109/cvprw53098.2021.00337}, + year = {2021}, + source = {Crossref}, + month = jun, } @article{coleman2017dawnbench, @@ -81,20 +89,21 @@ @article{coleman2017dawnbench title = {Analysis of {DAWNBench,} a Time-to-Accuracy Machine Learning Performance Benchmark}, url = {https://doi.org/10.1145/3352020.3352024}, volume = {53}, - year = {2019} + year = {2019}, + month = jul, } @inproceedings{coleman2022similarity, - author = {Cody Coleman and Edward Chou and Julian Katz{-}Samuels and Sean Culatana and Peter Bailis and Alexander C. Berg and Robert D. Nowak and Roshan Sumbaly and Matei Zaharia and I. Zeki Yalniz}, + author = {Coleman, Cody and Chou, Edward and Katz-Samuels, Julian and Culatana, Sean and Bailis, Peter and Berg, Alexander C. and Nowak, Robert D. and Sumbaly, Roshan and Zaharia, Matei and Yalniz, I. Zeki}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/aaai/ColemanCKCBBNSZ22.bib}, - booktitle = {Thirty-Sixth {AAAI} Conference on Artificial Intelligence, {AAAI} 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, {IAAI} 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, {EAAI} 2022 Virtual Event, February 22 - March 1, 2022}, + booktitle = {Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022}, pages = {6402--6410}, - publisher = {{AAAI} Press}, + publisher = {AAAI Press}, timestamp = {Mon, 11 Jul 2022 01:00:00 +0200}, title = {Similarity Search for Efficient Active Learning and Search of Rare Concepts}, url = {https://ojs.aaai.org/index.php/AAAI/article/view/20591}, - year = {2022} + year = {2022}, } @article{david2021tensorflow, @@ -103,7 +112,7 @@ @article{david2021tensorflow pages = {800--811}, title = {Tensorflow lite micro: {Embedded} machine learning for tinyml systems}, volume = {3}, - year = {2021} + year = {2021}, } @article{davies2018loihi, @@ -118,19 +127,21 @@ @article{davies2018loihi title = {Loihi: {A} Neuromorphic Manycore Processor with On-Chip Learning}, url = {https://doi.org/10.1109/mm.2018.112130359}, volume = {38}, - year = {2018} + year = {2018}, + month = jan, } @inproceedings{devlin2018bert, + author = {Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, address = {Minneapolis, Minnesota}, - author = {Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, - booktitle = {Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)}, - doi = {10.18653/v1/N19-1423}, + booktitle = {Proceedings of the 2019 Conference of the North}, + doi = {10.18653/v1/n19-1423}, pages = {4171--4186}, publisher = {Association for Computational Linguistics}, - title = {{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding}, - url = {https://aclanthology.org/N19-1423}, - year = {2019} + title = {{BERT:} {Pre-training} of Deep Bidirectional Transformers for Language Understanding}, + url = {https://doi.org/10.18653/v1/n19-1423}, + year = {2019}, + source = {Crossref}, } @article{gaviria2022dollar, @@ -145,21 +156,24 @@ @article{gaviria2022dollar title = {{MLPerf:} {An} Industry Standard Benchmark Suite for Machine Learning Performance}, url = {https://doi.org/10.1109/mm.2020.2974843}, volume = {40}, - year = {2020} + year = {2020}, + month = mar, } @inproceedings{hendrycks2021natural, - author = {Dan Hendrycks and Kevin Zhao and Steven Basart and Jacob Steinhardt and Dawn Song}, + author = {Hendrycks, Dan and Zhao, Kevin and Basart, Steven and Steinhardt, Jacob and Song, Dawn}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/cvpr/HendrycksZBSS21.bib}, - booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2021, virtual, June 19-25, 2021}, - doi = {10.1109/CVPR46437.2021.01501}, + booktitle = {2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, + doi = {10.1109/cvpr46437.2021.01501}, pages = {15262--15271}, - publisher = {Computer Vision Foundation / {IEEE}}, + publisher = {IEEE}, timestamp = {Mon, 18 Jul 2022 01:00:00 +0200}, title = {Natural Adversarial Examples}, - url = {https://openaccess.thecvf.com/content/CVPR2021/html/Hendrycks\_Natural\_Adversarial\_Examples\_CVPR\_2021\_paper.html}, - year = {2021} + url = {https://doi.org/10.1109/cvpr46437.2021.01501}, + year = {2021}, + source = {Crossref}, + month = jun, } @inproceedings{ignatov2018ai, @@ -171,35 +185,37 @@ @inproceedings{ignatov2018ai source = {Crossref}, title = {{AI} Benchmark: {All} About Deep Learning on Smartphones in 2019}, url = {https://doi.org/10.1109/iccvw.2019.00447}, - year = {2019} + year = {2019}, + month = oct, } @inproceedings{kiela2021dynabench, + author = {Kiela, Douwe and Bartolo, Max and Nie, Yixin and Kaushik, Divyansh and Geiger, Atticus and Wu, Zhengxuan and Vidgen, Bertie and Prasad, Grusha and Singh, Amanpreet and Ringshia, Pratik and Ma, Zhiyi and Thrush, Tristan and Riedel, Sebastian and Waseem, Zeerak and Stenetorp, Pontus and Jia, Robin and Bansal, Mohit and Potts, Christopher and Williams, Adina}, address = {Online}, - author = {Kiela, Douwe and Bartolo, Max and Nie, Yixin and Kaushik, Divyansh and Geiger, Atticus and Wu, Zhengxuan and Vidgen, Bertie and Prasad, Grusha and Singh, Amanpreet and Ringshia, Pratik and Ma, Zhiyi and Thrush, Tristan and Riedel, Sebastian and Waseem, Zeerak and Stenetorp, Pontus and Jia, Robin and Bansal, Mohit and Potts, Christopher and Williams, Adina}, booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, doi = {10.18653/v1/2021.naacl-main.324}, pages = {4110--4124}, publisher = {Association for Computational Linguistics}, - title = {Dynabench: Rethinking Benchmarking in {NLP}}, - url = {https://aclanthology.org/2021.naacl-main.324}, - year = {2021} + title = {Dynabench: {Rethinking} Benchmarking in {NLP}}, + url = {https://doi.org/10.18653/v1/2021.naacl-main.324}, + year = {2021}, + source = {Crossref}, } @inproceedings{koh2021wilds, - author = {Pang Wei Koh and Shiori Sagawa and Henrik Marklund and Sang Michael Xie and Marvin Zhang and Akshay Balsubramani and Weihua Hu and Michihiro Yasunaga and Richard Lanas Phillips and Irena Gao and Tony Lee and Etienne David and Ian Stavness and Wei Guo and Berton Earnshaw and Imran S. Haque and Sara M. Beery and Jure Leskovec and Anshul Kundaje and Emma Pierson and Sergey Levine and Chelsea Finn and Percy Liang}, + author = {Koh, Pang Wei and Sagawa, Shiori and Marklund, Henrik and Xie, Sang Michael and Zhang, Marvin and Balsubramani, Akshay and Hu, Weihua and Yasunaga, Michihiro and Phillips, Richard Lanas and Gao, Irena and Lee, Tony and David, Etienne and Stavness, Ian and Guo, Wei and Earnshaw, Berton and Haque, Imran S. and Beery, Sara M. and Leskovec, Jure and Kundaje, Anshul and Pierson, Emma and Levine, Sergey and Finn, Chelsea and Liang, Percy}, + editor = {Meila, Marina and Zhang, Tong}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/icml/KohSMXZBHYPGLDS21.bib}, - booktitle = {Proceedings of the 38th International Conference on Machine Learning, {ICML} 2021, 18-24 July 2021, Virtual Event}, - editor = {Marina Meila and Tong Zhang}, + booktitle = {Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event}, pages = {5637--5664}, - publisher = {{PMLR}}, + publisher = {PMLR}, series = {Proceedings of Machine Learning Research}, timestamp = {Tue, 13 Dec 2022 00:00:00 +0100}, title = {{WILDS:} {A} Benchmark of in-the-Wild Distribution Shifts}, url = {http://proceedings.mlr.press/v139/koh21a.html}, volume = {139}, - year = {2021} + year = {2021}, } @inproceedings{lin2014microsoft, @@ -208,20 +224,20 @@ @inproceedings{lin2014microsoft organization = {Springer}, pages = {740--755}, title = {Microsoft coco: {Common} objects in context}, - year = {2014} + year = {2014}, } @inproceedings{lundberg2017unified, - author = {Scott M. Lundberg and Su{-}In Lee}, + author = {Lundberg, Scott M. and Lee, Su-In}, + editor = {Guyon, Isabelle and von Luxburg, Ulrike and Bengio, Samy and Wallach, Hanna M. and Fergus, Rob and Vishwanathan, S. V. N. and Garnett, Roman}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/LundbergL17.bib}, - booktitle = {Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, {USA}}, - editor = {Isabelle Guyon and Ulrike von Luxburg and Samy Bengio and Hanna M. Wallach and Rob Fergus and S. V. N. Vishwanathan and Roman Garnett}, + booktitle = {Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA}, pages = {4765--4774}, timestamp = {Thu, 21 Jan 2021 00:00:00 +0100}, title = {A Unified Approach to Interpreting Model Predictions}, url = {https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html}, - year = {2017} + year = {2017}, } @article{maass1997networks, @@ -236,7 +252,8 @@ @article{maass1997networks title = {Networks of spiking neurons: {The} third generation of neural network models}, url = {https://doi.org/10.1016/s0893-6080(97)00011-7}, volume = {10}, - year = {1997} + year = {1997}, + month = dec, } @article{mattson2020mlperf, @@ -251,7 +268,8 @@ @article{mattson2020mlperf title = {{MLPerf:} {An} Industry Standard Benchmark Suite for Machine Learning Performance}, url = {https://doi.org/10.1109/mm.2020.2974843}, volume = {40}, - year = {2020} + year = {2020}, + month = mar, } @article{modha2023neural, @@ -266,7 +284,8 @@ @article{modha2023neural title = {Neural inference at the frontier of energy, space, and time}, url = {https://doi.org/10.1126/science.adh1174}, volume = {382}, - year = {2023} + year = {2023}, + month = oct, } @inproceedings{reddi2020mlperf, @@ -279,7 +298,8 @@ @inproceedings{reddi2020mlperf source = {Crossref}, title = {{MLPerf} Inference Benchmark}, url = {https://doi.org/10.1109/isca45697.2020.00045}, - year = {2020} + year = {2020}, + month = may, } @inproceedings{ribeiro2016should, @@ -287,7 +307,7 @@ @inproceedings{ribeiro2016should booktitle = {Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining}, pages = {1135--1144}, title = {{\textquotedblright} Why should i trust you?{\textquotedblright} Explaining the predictions of any classifier}, - year = {2016} + year = {2016}, } @article{schuman2022opportunities, @@ -302,7 +322,8 @@ @article{schuman2022opportunities title = {Opportunities for neuromorphic computing algorithms and applications}, url = {https://doi.org/10.1038/s43588-021-00184-y}, volume = {2}, - year = {2022} + year = {2022}, + month = jan, } @article{warden2018speech, @@ -311,21 +332,23 @@ @article{warden2018speech title = {Speech commands: {A} dataset for limited-vocabulary speech recognition}, url = {https://arxiv.org/abs/1804.03209}, volume = {abs/1804.03209}, - year = {2018} + year = {2018}, } @inproceedings{xie2020adversarial, - author = {Cihang Xie and Mingxing Tan and Boqing Gong and Jiang Wang and Alan L. Yuille and Quoc V. Le}, + author = {Xie, Cihang and Tan, Mingxing and Gong, Boqing and Wang, Jiang and Yuille, Alan L. and Le, Quoc V.}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/cvpr/XieTGWYL20.bib}, - booktitle = {2020 {IEEE/CVF} Conference on Computer Vision and Pattern Recognition, {CVPR} 2020, Seattle, WA, USA, June 13-19, 2020}, - doi = {10.1109/CVPR42600.2020.00090}, + booktitle = {2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, + doi = {10.1109/cvpr42600.2020.00090}, pages = {816--825}, - publisher = {{IEEE}}, + publisher = {IEEE}, timestamp = {Tue, 13 Oct 2020 01:00:00 +0200}, title = {Adversarial Examples Improve Image Recognition}, - url = {https://doi.org/10.1109/CVPR42600.2020.00090}, - year = {2020} + url = {https://doi.org/10.1109/cvpr42600.2020.00090}, + year = {2020}, + source = {Crossref}, + month = jun, } @article{xu2023demystifying, @@ -334,14 +357,14 @@ @article{xu2023demystifying title = {Demystifying {CLIP} Data}, url = {https://arxiv.org/abs/2309.16671}, volume = {abs/2309.16671}, - year = {2023} + year = {2023}, } @misc{yik2023neurobench, - archiveprefix = {arXiv}, author = {Yik, Jason and Ahmed, Soikat Hasan and Ahmed, Zergham and Anderson, Brian and Andreou, Andreas G. and Bartolozzi, Chiara and Basu, Arindam and den Blanken, Douwe and Bogdan, Petrut and Bohte, Sander and Bouhadjar, Younes and Buckley, Sonia and Cauwenberghs, Gert and Corradi, Federico and de Croon, Guido and Danielescu, Andreea and Daram, Anurag and Davies, Mike and Demirag, Yigit and Eshraghian, Jason and Forest, Jeremy and Furber, Steve and Furlong, Michael and Gilra, Aditya and Indiveri, Giacomo and Joshi, Siddharth and Karia, Vedant and Khacef, Lyes and Knight, James C. and Kriener, Laura and Kubendran, Rajkumar and Kudithipudi, Dhireesha and Lenz, Gregor and Manohar, Rajit and Mayr, Christian and Michmizos, Konstantinos and Muir, Dylan and Neftci, Emre and Nowotny, Thomas and Ottati, Fabrizio and Ozcelikkale, Ayca and Pacik-Nelson, Noah and Panda, Priyadarshini and Pao-Sheng, Sun and Payvand, Melika and Pehle, Christian and Petrovici, Mihai A. and Posch, Christoph and Renner, Alpha and Sandamirskaya, Yulia and Schaefer, Clemens JS and van Schaik, Andr\'e and Schemmel, Johannes and Schuman, Catherine and Seo, Jae-sun and Sheik, Sadique and Shrestha, Sumit Bam and Sifalakis, Manolis and Sironi, Amos and Stewart, Kenneth and Stewart, Terrence C. and Stratmann, Philipp and Tang, Guangzhi and Timcheck, Jonathan and Verhelst, Marian and Vineyard, Craig M. and Vogginger, Bernhard and Yousefzadeh, Amirreza and Zhou, Biyan and Zohora, Fatima Tuz and Frenkel, Charlotte and Reddi, Vijay Janapa}, + archiveprefix = {arXiv}, eprint = {2304.04640}, primaryclass = {cs.AI}, title = {{NeuroBench:} {Advancing} Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking}, - year = {2023} + year = {2023}, } diff --git a/contents/data_engineering/data_engineering.bib b/contents/data_engineering/data_engineering.bib index 9565b12a..a4d6ab8f 100644 --- a/contents/data_engineering/data_engineering.bib +++ b/contents/data_engineering/data_engineering.bib @@ -1,3 +1,6 @@ +%comment{This file was created with betterbib v5.0.11.} + + @article{aledhari2020federated, author = {Aledhari, Mohammed and Razzak, Rehma and Parizi, Reza M. and Saeed, Fahad}, bdsk-url-1 = {https://doi.org/10.1109/access.2020.3013541}, @@ -10,33 +13,36 @@ @article{aledhari2020federated title = {Federated Learning: {A} Survey on Enabling Technologies, Protocols, and Applications}, url = {https://doi.org/10.1109/access.2020.3013541}, volume = {8}, - year = {2020} + year = {2020}, } @inproceedings{ardila2020common, + author = {Ardila, Rosana and Branson, Megan and Davis, Kelly and Kohler, Michael and Meyer, Josh and Henretty, Michael and Morais, Reuben and Saunders, Lindsay and Tyers, Francis and Weber, Gregor}, address = {Marseille, France}, - author = {Ardila, Rosana and Branson, Megan and Davis, Kelly and Kohler, Michael and Meyer, Josh and Henretty, Michael and Morais, Reuben and Saunders, Lindsay and Tyers, Francis and Weber, Gregor}, booktitle = {Proceedings of the Twelfth Language Resources and Evaluation Conference}, isbn = {979-10-95546-34-4}, language = {English}, pages = {4218--4222}, publisher = {European Language Resources Association}, - title = {Common Voice: A Massively-Multilingual Speech Corpus}, + title = {Common Voice: {A} Massively-Multilingual Speech Corpus}, url = {https://aclanthology.org/2020.lrec-1.520}, - year = {2020} + year = {2020}, } @article{bender2018data, + author = {Bender, Emily M. and Friedman, Batya}, address = {Cambridge, MA}, - author = {Bender, Emily M. and Friedman, Batya}, - doi = {10.1162/tacl\_a\_00041}, + doi = {10.1162/tacl_a_00041}, journal = {Transactions of the Association for Computational Linguistics}, pages = {587--604}, - publisher = {MIT Press}, - title = {Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science}, - url = {https://aclanthology.org/Q18-1041}, + publisher = {MIT Press - Journals}, + title = {Data Statements for Natural Language Processing: {Toward} Mitigating System Bias and Enabling Better Science}, + url = {https://doi.org/10.1162/tacl_a_00041}, volume = {6}, - year = {2018} + year = {2018}, + source = {Crossref}, + issn = {2307-387X}, + month = dec, } @article{chapelle2009semisupervised, @@ -52,7 +58,8 @@ @article{chapelle2009semisupervised title = {Semi-Supervised Learning {(Chapelle,} {O.} et al., Eds.; 2006) {[Book} reviews]}, url = {https://doi.org/10.1109/tnn.2009.2015974}, volume = {20}, - year = {2009} + year = {2009}, + month = mar, } @article{gebru2021datasheets, @@ -68,14 +75,15 @@ @article{gebru2021datasheets title = {Datasheets for datasets}, url = {https://doi.org/10.1145/3458723}, volume = {64}, - year = {2021} + year = {2021}, + month = nov, } @misc{googleinformation, author = {Google}, bdsk-url-1 = {https://blog.google/documents/83/}, title = {Information quality content moderation}, - url = {https://blog.google/documents/83/} + url = {https://blog.google/documents/83/}, } @incollection{holland2020dataset, @@ -90,7 +98,7 @@ @incollection{holland2020dataset subtitle = {A Framework to Drive Higher Data Quality Standards}, title = {The Dataset Nutrition Label}, url = {https://doi.org/10.5040/9781509932771.ch-001}, - year = {2020} + year = {2020}, } @inproceedings{johnsonroberson2017driving, @@ -103,7 +111,8 @@ @inproceedings{johnsonroberson2017driving source = {Crossref}, title = {Driving in the Matrix: {Can} virtual worlds replace human-generated annotations for real world tasks?}, url = {https://doi.org/10.1109/icra.2017.7989092}, - year = {2017} + year = {2017}, + month = may, } @article{krishnan2022selfsupervised, @@ -119,7 +128,8 @@ @article{krishnan2022selfsupervised title = {Self-supervised learning in medicine and healthcare}, url = {https://doi.org/10.1038/s41551-022-00914-1}, volume = {6}, - year = {2022} + year = {2022}, + month = aug, } @article{northcutt2021pervasive, @@ -128,7 +138,7 @@ @article{northcutt2021pervasive doi = {https://doi.org/10.48550/arXiv.2103.14749 arXiv-issued DOI via DataCite}, journal = {arXiv}, title = {Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks}, - year = {2021} + year = {2021}, } @inproceedings{pushkarna2022data, @@ -141,7 +151,8 @@ @inproceedings{pushkarna2022data source = {Crossref}, title = {Data Cards: {Purposeful} and Transparent Dataset Documentation for Responsible {AI}}, url = {https://doi.org/10.1145/3531146.3533231}, - year = {2022} + year = {2022}, + month = jun, } @inproceedings{ratner2018snorkel, @@ -154,19 +165,26 @@ @inproceedings{ratner2018snorkel subtitle = {Weak Supervision for Multi-Task Learning}, title = {Snorkel {MeTaL}}, url = {https://doi.org/10.1145/3209889.3209898}, - year = {2018} + year = {2018}, + month = jun, } -@inproceedings{victor2019machine, - author = {Victor S. Sheng and Jing Zhang}, +@article{victor2019machine, + author = {Sheng, Victor S. and Zhang, Jing}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/aaai/Sheng019.bib}, - booktitle = {The Thirty-Third {AAAI} Conference on Artificial Intelligence, {AAAI} 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, {IAAI} 2019, The Ninth {AAAI} Symposium on Educational Advances in Artificial Intelligence, {EAAI} 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019}, + booktitle = {The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019}, doi = {10.1609/aaai.v33i01.33019837}, pages = {9837--9843}, - publisher = {{AAAI} Press}, + publisher = {Association for the Advancement of Artificial Intelligence (AAAI)}, timestamp = {Wed, 25 Sep 2019 01:00:00 +0200}, title = {Machine Learning with Crowdsourcing: {A} Brief Summary of the Past Research and Future Directions}, url = {https://doi.org/10.1609/aaai.v33i01.33019837}, - year = {2019} + year = {2019}, + number = {01}, + source = {Crossref}, + volume = {33}, + journal = {Proceedings of the AAAI Conference on Artificial Intelligence}, + issn = {2374-3468, 2159-5399}, + month = jul, } diff --git a/contents/dl_primer/dl_primer.bib b/contents/dl_primer/dl_primer.bib index a62cccf9..0486c4f7 100644 --- a/contents/dl_primer/dl_primer.bib +++ b/contents/dl_primer/dl_primer.bib @@ -1,10 +1,13 @@ +%comment{This file was created with betterbib v5.0.11.} + + @article{bank2023autoencoders, author = {Bank, Dor and Koenigstein, Noam and Giryes, Raja}, journal = {Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook}, pages = {353--374}, publisher = {Springer}, title = {Autoencoders}, - year = {2023} + year = {2023}, } @article{goodfellow2020generative, @@ -19,13 +22,14 @@ @article{goodfellow2020generative title = {Generative adversarial networks}, url = {https://doi.org/10.1145/3422622}, volume = {63}, - year = {2020} + year = {2020}, + month = oct, } @inproceedings{jouppi2017datacenter, + author = {Jouppi, Norman P. and Young, Cliff and Patil, Nishant and Patterson, David and Agrawal, Gaurav and Bajwa, Raminder and Bates, Sarah and Bhatia, Suresh and Boden, Nan and Borchers, Al and Boyle, Rick and Cantin, Pierre-luc and Chao, Clifford and Clark, Chris and Coriell, Jeremy and Daley, Mike and Dau, Matt and Dean, Jeffrey and Gelb, Ben and Ghaemmaghami, Tara Vazir and Gottipati, Rajendra and Gulland, William and Hagmann, Robert and Ho, C. Richard and Hogberg, Doug and Hu, John and Hundt, Robert and Hurt, Dan and Ibarz, Julian and Jaffey, Aaron and Jaworski, Alek and Kaplan, Alexander and Khaitan, Harshit and Killebrew, Daniel and Koch, Andy and Kumar, Naveen and Lacy, Steve and Laudon, James and Law, James and Le, Diemthu and Leary, Chris and Liu, Zhuyuan and Lucke, Kyle and Lundin, Alan and MacKean, Gordon and Maggiore, Adriana and Mahony, Maire and Miller, Kieran and Nagarajan, Rahul and Narayanaswami, Ravi and Ni, Ray and Nix, Kathy and Norrie, Thomas and Omernick, Mark and Penukonda, Narayana and Phelps, Andy and Ross, Jonathan and Ross, Matt and Salek, Amir and Samadiani, Emad and Severn, Chris and Sizikov, Gregory and Snelham, Matthew and Souter, Jed and Steinberg, Dan and Swing, Andy and Tan, Mercedes and Thorson, Gregory and Tian, Bo and Toma, Horia and Tuttle, Erick and Vasudevan, Vijay and Walter, Richard and Wang, Walter and Wilcox, Eric and Yoon, Doe Hyun}, abstract = {Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC{\textemdash}called a Tensor Processing Unit (TPU) {\textemdash} deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95\% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X {\textendash} 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X {\textendash} 80X higher. Moreover, using the CPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.}, address = {New York, NY, USA}, - author = {Jouppi, Norman P. and Young, Cliff and Patil, Nishant and Patterson, David and Agrawal, Gaurav and Bajwa, Raminder and Bates, Sarah and Bhatia, Suresh and Boden, Nan and Borchers, Al and Boyle, Rick and Cantin, Pierre-luc and Chao, Clifford and Clark, Chris and Coriell, Jeremy and Daley, Mike and Dau, Matt and Dean, Jeffrey and Gelb, Ben and Ghaemmaghami, Tara Vazir and Gottipati, Rajendra and Gulland, William and Hagmann, Robert and Ho, C. Richard and Hogberg, Doug and Hu, John and Hundt, Robert and Hurt, Dan and Ibarz, Julian and Jaffey, Aaron and Jaworski, Alek and Kaplan, Alexander and Khaitan, Harshit and Killebrew, Daniel and Koch, Andy and Kumar, Naveen and Lacy, Steve and Laudon, James and Law, James and Le, Diemthu and Leary, Chris and Liu, Zhuyuan and Lucke, Kyle and Lundin, Alan and MacKean, Gordon and Maggiore, Adriana and Mahony, Maire and Miller, Kieran and Nagarajan, Rahul and Narayanaswami, Ravi and Ni, Ray and Nix, Kathy and Norrie, Thomas and Omernick, Mark and Penukonda, Narayana and Phelps, Andy and Ross, Jonathan and Ross, Matt and Salek, Amir and Samadiani, Emad and Severn, Chris and Sizikov, Gregory and Snelham, Matthew and Souter, Jed and Steinberg, Dan and Swing, Andy and Tan, Mercedes and Thorson, Gregory and Tian, Bo and Toma, Horia and Tuttle, Erick and Vasudevan, Vijay and Walter, Richard and Wang, Walter and Wilcox, Eric and Yoon, Doe Hyun}, bdsk-url-1 = {https://doi.org/10.1145/3079856.3080246}, booktitle = {Proceedings of the 44th Annual International Symposium on Computer Architecture}, doi = {10.1145/3079856.3080246}, @@ -39,27 +43,28 @@ @inproceedings{jouppi2017datacenter source = {Crossref}, title = {In-Datacenter Performance Analysis of a Tensor Processing Unit}, url = {https://doi.org/10.1145/3079856.3080246}, - year = {2017} + year = {2017}, + month = jun, } @inproceedings{krizhevsky2012imagenet, - author = {Alex Krizhevsky and Ilya Sutskever and Geoffrey E. Hinton}, + author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E.}, + editor = {Bartlett, Peter L. and Pereira, Fernando C. N. and Burges, Christopher J. C. and Bottou, L\'eon and Weinberger, Kilian Q.}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/KrizhevskySH12.bib}, booktitle = {Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States}, - editor = {Peter L. Bartlett and Fernando C. N. Pereira and Christopher J. C. Burges and L{\'{e}}on Bottou and Kilian Q. Weinberger}, pages = {1106--1114}, timestamp = {Thu, 21 Jan 2021 00:00:00 +0100}, - title = {ImageNet Classification with Deep Convolutional Neural Networks}, + title = {{ImageNet} Classification with Deep Convolutional Neural Networks}, url = {https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html}, - year = {2012} + year = {2012}, } @book{rosenblatt1957perceptron, author = {Rosenblatt, Frank}, publisher = {Cornell Aeronautical Laboratory}, title = {The perceptron, a perceiving and recognizing automaton Project Para}, - year = {1957} + year = {1957}, } @article{rumelhart1986learning, @@ -74,18 +79,14 @@ @article{rumelhart1986learning title = {Learning representations by back-propagating errors}, url = {https://doi.org/10.1038/323533a0}, volume = {323}, - year = {1986} + year = {1986}, + month = oct, } -@inproceedings{vaswani2017attention, - author = {Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin}, - bibsource = {dblp computer science bibliography, https://dblp.org}, - biburl = {https://dblp.org/rec/conf/nips/VaswaniSPUJGKP17.bib}, - booktitle = {Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, {USA}}, - editor = {Isabelle Guyon and Ulrike von Luxburg and Samy Bengio and Hanna M. Wallach and Rob Fergus and S. V. N. Vishwanathan and Roman Garnett}, - pages = {5998--6008}, - timestamp = {Thu, 21 Jan 2021 00:00:00 +0100}, - title = {Attention is All you Need}, - url = {https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html}, - year = {2017} +@article{vaswani2017attention, + title={Attention is all you need}, + author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia}, + journal={Advances in neural information processing systems}, + volume={30}, + year={2017} } diff --git a/contents/dsp_spectral_features_block/dsp_spectral_features_block.bib b/contents/dsp_spectral_features_block/dsp_spectral_features_block.bib index e69de29b..00614696 100644 --- a/contents/dsp_spectral_features_block/dsp_spectral_features_block.bib +++ b/contents/dsp_spectral_features_block/dsp_spectral_features_block.bib @@ -0,0 +1,2 @@ +%comment{This file was created with betterbib v5.0.11.} + diff --git a/contents/efficient_ai/efficient_ai.bib b/contents/efficient_ai/efficient_ai.bib index d3e52144..c737e486 100644 --- a/contents/efficient_ai/efficient_ai.bib +++ b/contents/efficient_ai/efficient_ai.bib @@ -1,3 +1,6 @@ +%comment{This file was created with betterbib v5.0.11.} + + @book{barroso2019datacenter, author = {Barroso, Luiz Andr\'e and H\"olzle, Urs and Ranganathan, Parthasarathy}, doi = {10.1007/978-3-031-01761-2}, @@ -8,37 +11,39 @@ @book{barroso2019datacenter subtitle = {Designing Warehouse-Scale Machines}, title = {The Datacenter as a Computer}, url = {https://doi.org/10.1007/978-3-031-01761-2}, - year = {2019} + year = {2019}, } @article{chowdhery2019visual, - author = {Chowdhery, Aakanksha and Warden, Pete and Shlens, Jonathon and Howard, Andrew and Rhodes, Rocky}, - journal = {arXiv preprint arXiv:1906.05721}, - title = {Visual wake words dataset}, - year = {2019} + title={Visual wake words dataset}, + author={Chowdhery, Aakanksha and Warden, Pete and Shlens, Jonathon and Howard, Andrew and Rhodes, Rocky}, + journal={arXiv preprint arXiv:1906.05721}, + year={2019} } @misc{han2016deep, - archiveprefix = {arXiv}, author = {Han, Song and Mao, Huizi and Dally, William J.}, + archiveprefix = {arXiv}, eprint = {1510.00149}, primaryclass = {cs.CV}, title = {Deep Compression: {Compressing} Deep Neural Networks with Pruning, Trained Quantization and {Huffman} Coding}, - year = {2016} + year = {2016}, } @inproceedings{he2016deep, - author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun}, + author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/cvpr/HeZRS16.bib}, - booktitle = {2016 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2016, Las Vegas, NV, USA, June 27-30, 2016}, - doi = {10.1109/CVPR.2016.90}, + booktitle = {2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + doi = {10.1109/cvpr.2016.90}, pages = {770--778}, - publisher = {{IEEE} Computer Society}, + publisher = {IEEE}, timestamp = {Wed, 17 Apr 2019 01:00:00 +0200}, title = {Deep Residual Learning for Image Recognition}, - url = {https://doi.org/10.1109/CVPR.2016.90}, - year = {2016} + url = {https://doi.org/10.1109/cvpr.2016.90}, + year = {2016}, + source = {Crossref}, + month = jun, } @misc{howard2017mobilenets, @@ -47,15 +52,20 @@ @misc{howard2017mobilenets title = {{MobileNets:} {Efficient} Convolutional Neural Networks for Mobile Vision Applications}, url = {https://arxiv.org/abs/1704.04861}, volume = {abs/1704.04861}, - year = {2017} + year = {2017}, } @inproceedings{hu2018squeeze, author = {Hu, Jie and Shen, Li and Sun, Gang}, - booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition}, + booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages = {7132--7141}, - title = {Squeeze-and-excitation networks}, - year = {2018} + title = {Squeeze-and-Excitation Networks}, + year = {2018}, + doi = {10.1109/cvpr.2018.00745}, + source = {Crossref}, + url = {https://doi.org/10.1109/cvpr.2018.00745}, + publisher = {IEEE}, + month = jun, } @article{iandola2016squeezenet, @@ -64,13 +74,13 @@ @article{iandola2016squeezenet title = {{SqueezeNet:} {Alexnet-level} accuracy with 50x fewer parameters and 0.5 {MB} model size}, url = {https://arxiv.org/abs/1602.07360}, volume = {abs/1602.07360}, - year = {2016} + year = {2016}, } @inproceedings{jouppi2017datacenter, + author = {Jouppi, Norman P. and Young, Cliff and Patil, Nishant and Patterson, David and Agrawal, Gaurav and Bajwa, Raminder and Bates, Sarah and Bhatia, Suresh and Boden, Nan and Borchers, Al and Boyle, Rick and Cantin, Pierre-luc and Chao, Clifford and Clark, Chris and Coriell, Jeremy and Daley, Mike and Dau, Matt and Dean, Jeffrey and Gelb, Ben and Ghaemmaghami, Tara Vazir and Gottipati, Rajendra and Gulland, William and Hagmann, Robert and Ho, C. Richard and Hogberg, Doug and Hu, John and Hundt, Robert and Hurt, Dan and Ibarz, Julian and Jaffey, Aaron and Jaworski, Alek and Kaplan, Alexander and Khaitan, Harshit and Killebrew, Daniel and Koch, Andy and Kumar, Naveen and Lacy, Steve and Laudon, James and Law, James and Le, Diemthu and Leary, Chris and Liu, Zhuyuan and Lucke, Kyle and Lundin, Alan and MacKean, Gordon and Maggiore, Adriana and Mahony, Maire and Miller, Kieran and Nagarajan, Rahul and Narayanaswami, Ravi and Ni, Ray and Nix, Kathy and Norrie, Thomas and Omernick, Mark and Penukonda, Narayana and Phelps, Andy and Ross, Jonathan and Ross, Matt and Salek, Amir and Samadiani, Emad and Severn, Chris and Sizikov, Gregory and Snelham, Matthew and Souter, Jed and Steinberg, Dan and Swing, Andy and Tan, Mercedes and Thorson, Gregory and Tian, Bo and Toma, Horia and Tuttle, Erick and Vasudevan, Vijay and Walter, Richard and Wang, Walter and Wilcox, Eric and Yoon, Doe Hyun}, abstract = {Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC{\textemdash}called a Tensor Processing Unit (TPU) {\textemdash} deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95\% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X {\textendash} 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X {\textendash} 80X higher. Moreover, using the CPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.}, address = {New York, NY, USA}, - author = {Jouppi, Norman P. and Young, Cliff and Patil, Nishant and Patterson, David and Agrawal, Gaurav and Bajwa, Raminder and Bates, Sarah and Bhatia, Suresh and Boden, Nan and Borchers, Al and Boyle, Rick and Cantin, Pierre-luc and Chao, Clifford and Clark, Chris and Coriell, Jeremy and Daley, Mike and Dau, Matt and Dean, Jeffrey and Gelb, Ben and Ghaemmaghami, Tara Vazir and Gottipati, Rajendra and Gulland, William and Hagmann, Robert and Ho, C. Richard and Hogberg, Doug and Hu, John and Hundt, Robert and Hurt, Dan and Ibarz, Julian and Jaffey, Aaron and Jaworski, Alek and Kaplan, Alexander and Khaitan, Harshit and Killebrew, Daniel and Koch, Andy and Kumar, Naveen and Lacy, Steve and Laudon, James and Law, James and Le, Diemthu and Leary, Chris and Liu, Zhuyuan and Lucke, Kyle and Lundin, Alan and MacKean, Gordon and Maggiore, Adriana and Mahony, Maire and Miller, Kieran and Nagarajan, Rahul and Narayanaswami, Ravi and Ni, Ray and Nix, Kathy and Norrie, Thomas and Omernick, Mark and Penukonda, Narayana and Phelps, Andy and Ross, Jonathan and Ross, Matt and Salek, Amir and Samadiani, Emad and Severn, Chris and Sizikov, Gregory and Snelham, Matthew and Souter, Jed and Steinberg, Dan and Swing, Andy and Tan, Mercedes and Thorson, Gregory and Tian, Bo and Toma, Horia and Tuttle, Erick and Vasudevan, Vijay and Walter, Richard and Wang, Walter and Wilcox, Eric and Yoon, Doe Hyun}, bdsk-url-1 = {https://doi.org/10.1145/3079856.3080246}, booktitle = {Proceedings of the 44th Annual International Symposium on Computer Architecture}, doi = {10.1145/3079856.3080246}, @@ -84,7 +94,8 @@ @inproceedings{jouppi2017datacenter source = {Crossref}, title = {In-Datacenter Performance Analysis of a Tensor Processing Unit}, url = {https://doi.org/10.1145/3079856.3080246}, - year = {2017} + year = {2017}, + month = jun, } @article{lecun1989optimal, @@ -92,7 +103,7 @@ @article{lecun1989optimal journal = {Adv Neural Inf Process Syst}, title = {Optimal brain damage}, volume = {2}, - year = {1989} + year = {1989}, } @article{li2019edge, @@ -107,54 +118,74 @@ @article{li2019edge title = {Edge {AI:} {On-demand} Accelerating Deep Neural Network Inference via Edge Computing}, url = {https://doi.org/10.1109/twc.2019.2946140}, volume = {19}, - year = {2020} + year = {2020}, + month = jan, } @inproceedings{lin2014microsoft, - author = {Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence}, - booktitle = {Computer Vision--ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13}, + author = {Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll\'ar, Piotr and Zitnick, C Lawrence}, + booktitle = {Computer Vision{\textendash}ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13}, organization = {Springer}, pages = {740--755}, - title = {Microsoft coco: Common objects in context}, - year = {2014} + title = {Microsoft coco: {Common} objects in context}, + year = {2014}, } @article{russakovsky2015imagenet, - author = {Russakovsky, Olga and Deng, Jia and Su, Hao and Krause, Jonathan and Satheesh, Sanjeev and Ma, Sean and Huang, Zhiheng and Karpathy, Andrej and Khosla, Aditya and Bernstein, Michael and others}, - journal = {International journal of computer vision}, + author = {Russakovsky, Olga and Deng, Jia and Su, Hao and Krause, Jonathan and Satheesh, Sanjeev and Ma, Sean and Huang, Zhiheng and Karpathy, Andrej and Khosla, Aditya and Bernstein, Michael and Berg, Alexander C. and Fei-Fei, Li}, + journal = {Int. J. Comput. Vision}, pages = {211--252}, - publisher = {Springer}, - title = {Imagenet large scale visual recognition challenge}, + publisher = {Springer Science and Business Media LLC}, + title = {{ImageNet} Large Scale Visual Recognition Challenge}, volume = {115}, - year = {2015} + year = {2015}, + doi = {10.1007/s11263-015-0816-y}, + number = {3}, + source = {Crossref}, + url = {https://doi.org/10.1007/s11263-015-0816-y}, + issn = {0920-5691, 1573-1405}, + month = apr, } @article{schizas2022tinyml, - author = {Schizas, Nikolaos and Karras, Aristeidis and Karras, Christos and Sioutas, Spyros}, - journal = {Future Internet}, - title = {TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review}, - doi = {https://doi.org/10.3390/fi14120363}, - year = {2022} + author = {Schizas, Nikolaos and Karras, Aristeidis and Karras, Christos and Sioutas, Spyros}, + journal = {Future Internet}, + title = {{TinyML} for Ultra-Low Power {AI} and Large Scale {IoT} Deployments: {A} Systematic Review}, + doi = {10.3390/fi14120363}, + year = {2022}, + number = {12}, + source = {Crossref}, + url = {https://doi.org/10.3390/fi14120363}, + volume = {14}, + publisher = {MDPI AG}, + issn = {1999-5903}, + pages = {363}, + month = dec, } @article{warden2018speech, author = {Warden, Pete}, journal = {arXiv preprint arXiv:1804.03209}, - title = {Speech commands: A dataset for limited-vocabulary speech recognition}, - year = {2018} + title = {Speech commands: {A} dataset for limited-vocabulary speech recognition}, + year = {2018}, } @book{warden2019tinyml, author = {Warden, Pete and Situnayake, Daniel}, publisher = {O'Reilly Media}, title = {Tinyml: {Machine} learning with tensorflow lite on arduino and ultra-low-power microcontrollers}, - year = {2019} + year = {2019}, } @inproceedings{xie2017aggregated, - author = {Xie, Saining and Girshick, Ross and Doll{\'a}r, Piotr and Tu, Zhuowen and He, Kaiming}, - booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition}, + author = {Xie, Saining and Girshick, Ross and Dollar, Piotr and Tu, Zhuowen and He, Kaiming}, + booktitle = {2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, pages = {1492--1500}, - title = {Aggregated residual transformations for deep neural networks}, - year = {2017} + title = {Aggregated Residual Transformations for Deep Neural Networks}, + year = {2017}, + doi = {10.1109/cvpr.2017.634}, + source = {Crossref}, + url = {https://doi.org/10.1109/cvpr.2017.634}, + publisher = {IEEE}, + month = jul, } diff --git a/contents/frameworks/frameworks.bib b/contents/frameworks/frameworks.bib index 71a37a67..3bc1c371 100644 --- a/contents/frameworks/frameworks.bib +++ b/contents/frameworks/frameworks.bib @@ -1,3 +1,6 @@ +%comment{This file was created with betterbib v5.0.11.} + + @inproceedings{abadi2016tensorflow, author = {Yu, Yuan and Abadi, Mart{\'\i}n and Barham, Paul and Brevdo, Eugene and Burrows, Mike and Davis, Andy and Dean, Jeff and Ghemawat, Sanjay and Harley, Tim and Hawkins, Peter and Isard, Michael and Kudlur, Manjunath and Monga, Rajat and Murray, Derek and Zheng, Xiaoqiang}, booktitle = {Proceedings of the Thirteenth EuroSys Conference}, @@ -7,35 +10,36 @@ @inproceedings{abadi2016tensorflow source = {Crossref}, title = {Dynamic control flow in large-scale machine learning}, url = {https://doi.org/10.1145/3190508.3190551}, - year = {2018} + year = {2018}, + month = apr, } @misc{al2016theano, - archiveprefix = {arXiv}, author = {Team, The Theano Development and Al-Rfou, Rami and Alain, Guillaume and Almahairi, Amjad and Angermueller, Christof and Bahdanau, Dzmitry and Ballas, Nicolas and Bastien, Fr\'ed\'eric and Bayer, Justin and Belikov, Anatoly and Belopolsky, Alexander and Bengio, Yoshua and Bergeron, Arnaud and Bergstra, James and Bisson, Valentin and Snyder, Josh Bleecher and Bouchard, Nicolas and Boulanger-Lewandowski, Nicolas and Bouthillier, Xavier and de Br\'ebisson, Alexandre and Breuleux, Olivier and Carrier, Pierre-Luc and Cho, Kyunghyun and Chorowski, Jan and Christiano, Paul and Cooijmans, Tim and C\^ot\'e, Marc-Alexandre and C\^ot\'e, Myriam and Courville, Aaron and Dauphin, Yann N. and Delalleau, Olivier and Demouth, Julien and Desjardins, Guillaume and Dieleman, Sander and Dinh, Laurent and Ducoffe, M\'elanie and Dumoulin, Vincent and Kahou, Samira Ebrahimi and Erhan, Dumitru and Fan, Ziye and Firat, Orhan and Germain, Mathieu and Glorot, Xavier and Goodfellow, Ian and Graham, Matt and Gulcehre, Caglar and Hamel, Philippe and Harlouchet, Iban and Heng, Jean-Philippe and Hidasi, Bal\'azs and Honari, Sina and Jain, Arjun and Jean, S\'ebastien and Jia, Kai and Korobov, Mikhail and Kulkarni, Vivek and Lamb, Alex and Lamblin, Pascal and Larsen, Eric and Laurent, C\'esar and Lee, Sean and Lefrancois, Simon and Lemieux, Simon and L\'eonard, Nicholas and Lin, Zhouhan and Livezey, Jesse A. and Lorenz, Cory and Lowin, Jeremiah and Ma, Qianli and Manzagol, Pierre-Antoine and Mastropietro, Olivier and McGibbon, Robert T. and Memisevic, Roland and van Merri\"enboer, Bart and Michalski, Vincent and Mirza, Mehdi and Orlandi, Alberto and Pal, Christopher and Pascanu, Razvan and Pezeshki, Mohammad and Raffel, Colin and Renshaw, Daniel and Rocklin, Matthew and Romero, Adriana and Roth, Markus and Sadowski, Peter and Salvatier, John and Savard, Fran\c{c}ois and Schl\"uter, Jan and Schulman, John and Schwartz, Gabriel and Serban, Iulian Vlad and Serdyuk, Dmitriy and Shabanian, Samira and Simon, \'Etienne and Spieckermann, Sigurd and Subramanyam, S. Ramana and Sygnowski, Jakub and Tanguay, J\'er\'emie and van Tulder, Gijs and Turian, Joseph and Urban, Sebastian and Vincent, Pascal and Visin, Francesco and de Vries, Harm and Warde-Farley, David and Webb, Dustin J. and Willson, Matthew and Xu, Kelvin and Xue, Lijun and Yao, Li and Zhang, Saizheng and Zhang, Ying}, + archiveprefix = {arXiv}, eprint = {1605.02688}, primaryclass = {cs.SC}, title = {Theano: {A} Python framework for fast computation of mathematical expressions}, - year = {2016} + year = {2016}, } @inproceedings{brown2020language, - author = {Tom B. Brown and Benjamin Mann and Nick Ryder and Melanie Subbiah and Jared Kaplan and Prafulla Dhariwal and Arvind Neelakantan and Pranav Shyam and Girish Sastry and Amanda Askell and Sandhini Agarwal and Ariel Herbert{-}Voss and Gretchen Krueger and Tom Henighan and Rewon Child and Aditya Ramesh and Daniel M. Ziegler and Jeffrey Wu and Clemens Winter and Christopher Hesse and Mark Chen and Eric Sigler and Mateusz Litwin and Scott Gray and Benjamin Chess and Jack Clark and Christopher Berner and Sam McCandlish and Alec Radford and Ilya Sutskever and Dario Amodei}, + author = {Brown, Tom B. and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel M. and Wu, Jeffrey and Winter, Clemens and Hesse, Christopher and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario}, + editor = {Larochelle, Hugo and Ranzato, Marc'Aurelio and Hadsell, Raia and Balcan, Maria-Florina and Lin, Hsuan-Tien}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/BrownMRSKDNSSAA20.bib}, booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual}, - editor = {Hugo Larochelle and Marc'Aurelio Ranzato and Raia Hadsell and Maria{-}Florina Balcan and Hsuan{-}Tien Lin}, timestamp = {Tue, 19 Jan 2021 00:00:00 +0100}, title = {Language Models are Few-Shot Learners}, url = {https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html}, - year = {2020} + year = {2020}, } @article{chollet2018keras, author = {Chollet, Fran\c{c}ois}, journal = {March 9th}, title = {Introduction to keras}, - year = {2018} + year = {2018}, } @article{david2021tensorflow, @@ -44,48 +48,52 @@ @article{david2021tensorflow pages = {800--811}, title = {Tensorflow lite micro: {Embedded} machine learning for tinyml systems}, volume = {3}, - year = {2021} + year = {2021}, } @inproceedings{dean2012large, - author = {Jeffrey Dean and Greg Corrado and Rajat Monga and Kai Chen and Matthieu Devin and Quoc V. Le and Mark Z. Mao and Marc'Aurelio Ranzato and Andrew W. Senior and Paul A. Tucker and Ke Yang and Andrew Y. Ng}, + author = {Dean, Jeffrey and Corrado, Greg and Monga, Rajat and Chen, Kai and Devin, Matthieu and Le, Quoc V. and Mao, Mark Z. and Ranzato, Marc'Aurelio and Senior, Andrew W. and Tucker, Paul A. and Yang, Ke and Ng, Andrew Y.}, + editor = {Bartlett, Peter L. and Pereira, Fernando C. N. and Burges, Christopher J. C. and Bottou, L\'eon and Weinberger, Kilian Q.}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/DeanCMCDLMRSTYN12.bib}, booktitle = {Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States}, - editor = {Peter L. Bartlett and Fernando C. N. Pereira and Christopher J. C. Burges and L{\'{e}}on Bottou and Kilian Q. Weinberger}, pages = {1232--1240}, timestamp = {Thu, 21 Jan 2021 00:00:00 +0100}, title = {Large Scale Distributed Deep Networks}, url = {https://proceedings.neurips.cc/paper/2012/hash/6aca97005c68f1206823815f66102863-Abstract.html}, - year = {2012} + year = {2012}, } @inproceedings{deng2009imagenet, - author = {Jia Deng and Wei Dong and Richard Socher and Li{-}Jia Li and Kai Li and Fei{-}Fei Li}, + author = {Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Li, Fei-Fei}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/cvpr/DengDSLL009.bib}, - booktitle = {2009 {IEEE} Computer Society Conference on Computer Vision and Pattern Recognition {(CVPR} 2009), 20-25 June 2009, Miami, Florida, {USA}}, - doi = {10.1109/CVPR.2009.5206848}, + booktitle = {2009 IEEE Conference on Computer Vision and Pattern Recognition}, + doi = {10.1109/cvpr.2009.5206848}, pages = {248--255}, - publisher = {{IEEE} Computer Society}, + publisher = {IEEE}, timestamp = {Fri, 27 Mar 2020 00:00:00 +0100}, - title = {ImageNet: {A} large-scale hierarchical image database}, - url = {https://doi.org/10.1109/CVPR.2009.5206848}, - year = {2009} + title = {{ImageNet:} {A} large-scale hierarchical image database}, + url = {https://doi.org/10.1109/cvpr.2009.5206848}, + year = {2009}, + source = {Crossref}, + month = jun, } @inproceedings{he2016deep, - author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun}, + author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/cvpr/HeZRS16.bib}, - booktitle = {2016 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2016, Las Vegas, NV, USA, June 27-30, 2016}, - doi = {10.1109/CVPR.2016.90}, + booktitle = {2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + doi = {10.1109/cvpr.2016.90}, pages = {770--778}, - publisher = {{IEEE} Computer Society}, + publisher = {IEEE}, timestamp = {Wed, 17 Apr 2019 01:00:00 +0200}, title = {Deep Residual Learning for Image Recognition}, - url = {https://doi.org/10.1109/CVPR.2016.90}, - year = {2016} + url = {https://doi.org/10.1109/cvpr.2016.90}, + year = {2016}, + source = {Crossref}, + month = jun, } @inproceedings{jia2014caffe, @@ -98,20 +106,21 @@ @inproceedings{jia2014caffe subtitle = {Convolutional Architecture for Fast Feature Embedding}, title = {Caffe}, url = {https://doi.org/10.1145/2647868.2654889}, - year = {2014} + year = {2014}, + month = nov, } @inproceedings{krizhevsky2012imagenet, - author = {Alex Krizhevsky and Ilya Sutskever and Geoffrey E. Hinton}, + author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E.}, + editor = {Bartlett, Peter L. and Pereira, Fernando C. N. and Burges, Christopher J. C. and Bottou, L\'eon and Weinberger, Kilian Q.}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/KrizhevskySH12.bib}, booktitle = {Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States}, - editor = {Peter L. Bartlett and Fernando C. N. Pereira and Christopher J. C. Burges and L{\'{e}}on Bottou and Kilian Q. Weinberger}, pages = {1106--1114}, timestamp = {Thu, 21 Jan 2021 00:00:00 +0100}, - title = {ImageNet Classification with Deep Convolutional Neural Networks}, + title = {{ImageNet} Classification with Deep Convolutional Neural Networks}, url = {https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html}, - year = {2012} + year = {2012}, } @inproceedings{kung1979systolic, @@ -121,7 +130,7 @@ @inproceedings{kung1979systolic pages = {256--282}, title = {Systolic arrays (for {VLSI)}}, volume = {1}, - year = {1979} + year = {1979}, } @article{lai2018cmsis, @@ -130,20 +139,20 @@ @article{lai2018cmsis title = {Cmsis-nn: {Efficient} neural network kernels for arm cortex-m cpus}, url = {https://arxiv.org/abs/1801.06601}, volume = {abs/1801.06601}, - year = {2018} + year = {2018}, } @inproceedings{li2014communication, - author = {Mu Li and David G. Andersen and Alexander J. Smola and Kai Yu}, + author = {Li, Mu and Andersen, David G. and Smola, Alexander J. and Yu, Kai}, + editor = {Ghahramani, Zoubin and Welling, Max and Cortes, Corinna and Lawrence, Neil D. and Weinberger, Kilian Q.}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/LiASY14.bib}, booktitle = {Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada}, - editor = {Zoubin Ghahramani and Max Welling and Corinna Cortes and Neil D. Lawrence and Kilian Q. Weinberger}, pages = {19--27}, timestamp = {Thu, 21 Jan 2021 00:00:00 +0100}, title = {Communication Efficient Distributed Machine Learning with the Parameter Server}, url = {https://proceedings.neurips.cc/paper/2014/hash/1ff1de774005f8da13f42943881c655f-Abstract.html}, - year = {2014} + year = {2014}, } @article{li2017learning, @@ -158,48 +167,53 @@ @article{li2017learning title = {Learning without Forgetting}, url = {https://doi.org/10.1109/tpami.2017.2773081}, volume = {40}, - year = {2018} + year = {2018}, + month = dec, } @inproceedings{lin2020mcunet, - author = {Ji Lin and Wei{-}Ming Chen and Yujun Lin and John Cohn and Chuang Gan and Song Han}, + author = {Lin, Ji and Chen, Wei-Ming and Lin, Yujun and Cohn, John and Gan, Chuang and Han, Song}, + editor = {Larochelle, Hugo and Ranzato, Marc'Aurelio and Hadsell, Raia and Balcan, Maria-Florina and Lin, Hsuan-Tien}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/LinCLCG020.bib}, booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual}, - editor = {Hugo Larochelle and Marc'Aurelio Ranzato and Raia Hadsell and Maria{-}Florina Balcan and Hsuan{-}Tien Lin}, timestamp = {Thu, 11 Feb 2021 00:00:00 +0100}, - title = {MCUNet: Tiny Deep Learning on IoT Devices}, + title = {{MCUNet:} {Tiny} Deep Learning on {IoT} Devices}, url = {https://proceedings.neurips.cc/paper/2020/hash/86c51678350f656dcc7f490a43946ee5-Abstract.html}, - year = {2020} + year = {2020}, } @inproceedings{mcmahan2023communicationefficient, - author = {Brendan McMahan and Eider Moore and Daniel Ramage and Seth Hampson and Blaise Ag{\"{u}}era y Arcas}, + author = {McMahan, Brendan and Moore, Eider and Ramage, Daniel and Hampson, Seth and y Arcas, Blaise Ag\"uera}, + editor = {Singh, Aarti and Zhu, Xiaojin (Jerry)}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/aistats/McMahanMRHA17.bib}, - booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, {AISTATS} 2017, 20-22 April 2017, Fort Lauderdale, FL, {USA}}, - editor = {Aarti Singh and Xiaojin (Jerry) Zhu}, + booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20-22 April 2017, Fort Lauderdale, FL, USA}, pages = {1273--1282}, - publisher = {{PMLR}}, + publisher = {PMLR}, series = {Proceedings of Machine Learning Research}, timestamp = {Wed, 03 Apr 2019 01:00:00 +0200}, title = {Communication-Efficient Learning of Deep Networks from Decentralized Data}, url = {http://proceedings.mlr.press/v54/mcmahan17a.html}, volume = {54}, - year = {2017} + year = {2017}, } @inproceedings{paszke2019pytorch, - author = {Adam Paszke and Sam Gross and Francisco Massa and Adam Lerer and James Bradbury and Gregory Chanan and Trevor Killeen and Zeming Lin and Natalia Gimelshein and Luca Antiga and Alban Desmaison and Andreas K{\"{o}}pf and Edward Yang and Zachary DeVito and Martin Raison and Alykhan Tejani and Sasank Chilamkurthy and Benoit Steiner and Lu Fang and Junjie Bai and Soumith Chintala}, + author = {Ansel, Jason and Yang, Edward and He, Horace and Gimelshein, Natalia and Jain, Animesh and Voznesensky, Michael and Bao, Bin and Bell, Peter and Berard, David and Burovski, Evgeni and Chauhan, Geeta and Chourdia, Anjali and Constable, Will and Desmaison, Alban and DeVito, Zachary and Ellison, Elias and Feng, Will and Gong, Jiong and Gschwind, Michael and Hirsh, Brian and Huang, Sherlock and Kalambarkar, Kshiteej and Kirsch, Laurent and Lazos, Michael and Lezcano, Mario and Liang, Yanbo and Liang, Jason and Lu, Yinghai and Luk, C. K. and Maher, Bert and Pan, Yunjie and Puhrsch, Christian and Reso, Matthias and Saroufim, Mark and Siraichi, Marcos Yukio and Suk, Helen and Zhang, Shunting and Suo, Michael and Tillet, Phil and Zhao, Xu and Wang, Eikan and Zhou, Keren and Zou, Richard and Wang, Xiaodong and Mathews, Ajit and Wen, William and Chanan, Gregory and Wu, Peng and Chintala, Soumith}, + editor = {Wallach, Hanna M. and Larochelle, Hugo and Beygelzimer, Alina and d'Alch\'e-Buc, Florence and Fox, Emily B. and Garnett, Roman}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/PaszkeGMLBCKLGA19.bib}, - booktitle = {Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada}, - editor = {Hanna M. Wallach and Hugo Larochelle and Alina Beygelzimer and Florence d'Alch{\'{e}}{-}Buc and Emily B. Fox and Roman Garnett}, + booktitle = {Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2}, pages = {8024--8035}, timestamp = {Thu, 21 Jan 2021 00:00:00 +0100}, - title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library}, - url = {https://proceedings.neurips.cc/paper/2019/hash/bdbca288fee7f92f2bfa9f7012727740-Abstract.html}, - year = {2019} + title = {{PyTorch} 2: {Faster} Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation}, + url = {https://doi.org/10.1145/3620665.3640366}, + year = {2024}, + doi = {10.1145/3620665.3640366}, + source = {Crossref}, + publisher = {ACM}, + month = apr, } @inproceedings{seide2016cntk, @@ -212,7 +226,8 @@ @inproceedings{seide2016cntk subtitle = {Microsoft's Open-Source Deep-Learning Toolkit}, title = {Cntk}, url = {https://doi.org/10.1145/2939672.2945397}, - year = {2016} + year = {2016}, + month = aug, } @inproceedings{tokui2015chainer, @@ -226,5 +241,6 @@ @inproceedings{tokui2015chainer title = {Chainer}, url = {https://doi.org/10.1145/3292500.3330756}, volume = {5}, - year = {2019} + year = {2019}, + month = jul, } diff --git a/contents/generative_ai/generative_ai.bib b/contents/generative_ai/generative_ai.bib index e69de29b..00614696 100644 --- a/contents/generative_ai/generative_ai.bib +++ b/contents/generative_ai/generative_ai.bib @@ -0,0 +1,2 @@ +%comment{This file was created with betterbib v5.0.11.} + diff --git a/contents/hw_acceleration/hw_acceleration.bib b/contents/hw_acceleration/hw_acceleration.bib index c9152d12..41bebdb2 100644 --- a/contents/hw_acceleration/hw_acceleration.bib +++ b/contents/hw_acceleration/hw_acceleration.bib @@ -1,7 +1,16 @@ -@article{gwennap_certus-nx_nodate, - author = {Gwennap, Linley}, - language = {en}, - title = {Certus-{NX} Innovates General-Purpose {FPGAs}} +%comment{This file was created with betterbib v5.0.11.} + + +@inproceedings{Li2020Additive, + author = {Li, Yuhang and Dong, Xin and Wang, Wei}, + bibsource = {dblp computer science bibliography, https://dblp.org}, + biburl = {https://dblp.org/rec/conf/iclr/LiDW20.bib}, + booktitle = {8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020}, + publisher = {OpenReview.net}, + timestamp = {Tue, 18 Aug 2020 01:00:00 +0200}, + title = {Additive Powers-of-Two Quantization: {An} Efficient Non-uniform Discretization for Neural Networks}, + url = {https://openreview.net/forum?id=BkgXT24tDS}, + year = {2020}, } @inproceedings{adolf2016fathom, @@ -14,19 +23,26 @@ @inproceedings{adolf2016fathom source = {Crossref}, title = {Fathom: {Reference} workloads for modern deep learning methods}, url = {https://doi.org/10.1109/iiswc.2016.7581275}, - year = {2016} + year = {2016}, + month = sep, } @inproceedings{agnesina2023autodmp, author = {Agnesina, Anthony and Rajvanshi, Puranjay and Yang, Tian and Pradipta, Geraldo and Jiao, Austin and Keller, Ben and Khailany, Brucek and Ren, Haoxing}, booktitle = {Proceedings of the 2023 International Symposium on Physical Design}, pages = {149--157}, - title = {AutoDMP: Automated dreamplace-based macro placement}, - year = {2023} + title = {{AutoDMP}}, + year = {2023}, + doi = {10.1145/3569052.3578923}, + source = {Crossref}, + url = {https://doi.org/10.1145/3569052.3578923}, + publisher = {ACM}, + subtitle = {Automated DREAMPlace-based Macro Placement}, + month = mar, } @article{asit2021accelerating, - author = {Asit K. Mishra and Jorge Albericio Latorre and Jeff Pool and Darko Stosic and Dusan Stosic and Ganesh Venkatesh and Chong Yu and Paulius Micikevicius}, + author = {Mishra, Asit K. and Latorre, Jorge Albericio and Pool, Jeff and Stosic, Darko and Stosic, Dusan and Venkatesh, Ganesh and Yu, Chong and Micikevicius, Paulius}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/journals/corr/abs-2104-08378.bib}, eprint = {2104.08378}, @@ -36,7 +52,7 @@ @article{asit2021accelerating title = {Accelerating Sparse Deep Neural Networks}, url = {https://arxiv.org/abs/2104.08378}, volume = {abs/2104.08378}, - year = {2021} + year = {2021}, } @article{bains2020business, @@ -51,15 +67,21 @@ @article{bains2020business title = {The business of building brains}, url = {https://doi.org/10.1038/s41928-020-0449-1}, volume = {3}, - year = {2020} + year = {2020}, + month = jul, } @inproceedings{bhardwaj2020comprehensive, - author = {Bhardwaj, Kshitij and Havasi, Marton and Yao, Yuan and Brooks, David M and Hern{\'a}ndez-Lobato, Jos{\'e} Miguel and Wei, Gu-Yeon}, + author = {Bhardwaj, Kshitij and Havasi, Marton and Yao, Yuan and Brooks, David M. and Hern\'andez-Lobato, Jos\'e Miguel and Wei, Gu-Yeon}, booktitle = {Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design}, pages = {145--150}, - title = {A comprehensive methodology to determine optimal coherence interfaces for many-accelerator SoCs}, - year = {2020} + title = {A comprehensive methodology to determine optimal coherence interfaces for many-accelerator {SoCs}}, + year = {2020}, + doi = {10.1145/3370748.3406564}, + source = {Crossref}, + url = {https://doi.org/10.1145/3370748.3406564}, + publisher = {ACM}, + month = aug, } @article{biggs2021natively, @@ -74,7 +96,8 @@ @article{biggs2021natively title = {A natively flexible 32-bit Arm microprocessor}, url = {https://doi.org/10.1038/s41586-021-03625-w}, volume = {595}, - year = {2021} + year = {2021}, + month = jul, } @article{binkert2011gem5, @@ -89,19 +112,20 @@ @article{binkert2011gem5 title = {The gem5 simulator}, url = {https://doi.org/10.1145/2024716.2024718}, volume = {39}, - year = {2011} + year = {2011}, + month = may, } @inproceedings{brown2020language, - author = {Tom B. Brown and Benjamin Mann and Nick Ryder and Melanie Subbiah and Jared Kaplan and Prafulla Dhariwal and Arvind Neelakantan and Pranav Shyam and Girish Sastry and Amanda Askell and Sandhini Agarwal and Ariel Herbert{-}Voss and Gretchen Krueger and Tom Henighan and Rewon Child and Aditya Ramesh and Daniel M. Ziegler and Jeffrey Wu and Clemens Winter and Christopher Hesse and Mark Chen and Eric Sigler and Mateusz Litwin and Scott Gray and Benjamin Chess and Jack Clark and Christopher Berner and Sam McCandlish and Alec Radford and Ilya Sutskever and Dario Amodei}, + author = {Brown, Tom B. and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel M. and Wu, Jeffrey and Winter, Clemens and Hesse, Christopher and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario}, + editor = {Larochelle, Hugo and Ranzato, Marc'Aurelio and Hadsell, Raia and Balcan, Maria-Florina and Lin, Hsuan-Tien}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/BrownMRSKDNSSAA20.bib}, booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual}, - editor = {Hugo Larochelle and Marc'Aurelio Ranzato and Raia Hadsell and Maria{-}Florina Balcan and Hsuan{-}Tien Lin}, timestamp = {Tue, 19 Jan 2021 00:00:00 +0100}, title = {Language Models are Few-Shot Learners}, url = {https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html}, - year = {2020} + year = {2020}, } @article{burr2016recent, @@ -116,7 +140,8 @@ @article{burr2016recent title = {Recent Progress in Phase-{Change\ensuremath{<}?Pub} \_newline {?\ensuremath{>}Memory} Technology}, url = {https://doi.org/10.1109/jetcas.2016.2547718}, volume = {6}, - year = {2016} + year = {2016}, + month = jun, } @inproceedings{chen2018tvm, @@ -124,13 +149,13 @@ @inproceedings{chen2018tvm booktitle = {13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)}, pages = {578--594}, title = {{TVM:} {An} automated End-to-End optimizing compiler for deep learning}, - year = {2018} + year = {2018}, } @article{cheng2017survey, author = {Cheng, Yu and Wang, Duo and Zhou, Pan and Zhang, Tao}, doi = {10.1109/msp.2017.2765695}, - issn = {1053-5888}, + issn = {1053-5888, 1558-0792}, journal = {IEEE Signal Process Mag.}, number = {1}, pages = {126--136}, @@ -139,7 +164,8 @@ @article{cheng2017survey title = {Model Compression and Acceleration for Deep Neural Networks: {The} Principles, Progress, and Challenges}, url = {https://doi.org/10.1109/msp.2017.2765695}, volume = {35}, - year = {2018} + year = {2018}, + month = jan, } @article{chi2016prime, @@ -155,7 +181,8 @@ @article{chi2016prime title = {Prime}, url = {https://doi.org/10.1145/3007787.3001140}, volume = {44}, - year = {2016} + year = {2016}, + month = jun, } @article{chua1971memristor, @@ -170,7 +197,7 @@ @article{chua1971memristor title = {Memristor-The missing circuit element}, url = {https://doi.org/10.1109/tct.1971.1083337}, volume = {18}, - year = {1971} + year = {1971}, } @article{davies2018loihi, @@ -185,7 +212,8 @@ @article{davies2018loihi title = {Loihi: {A} Neuromorphic Manycore Processor with On-Chip Learning}, url = {https://doi.org/10.1109/mm.2018.112130359}, volume = {38}, - year = {2018} + year = {2018}, + month = jan, } @article{davies2021advancing, @@ -200,7 +228,8 @@ @article{davies2021advancing title = {Advancing Neuromorphic Computing With Loihi: {A} Survey of Results and Outlook}, url = {https://doi.org/10.1109/jproc.2021.3067593}, volume = {109}, - year = {2021} + year = {2021}, + month = may, } @article{dongarra2009evolution, @@ -209,7 +238,7 @@ @article{dongarra2009evolution pages = {3--4}, title = {The evolution of high performance computing on system z}, volume = {53}, - year = {2009} + year = {2009}, } @article{duarte2022fastml, @@ -218,7 +247,7 @@ @article{duarte2022fastml title = {{FastML} Science Benchmarks: {Accelerating} Real-Time Scientific Edge Machine Learning}, url = {https://arxiv.org/abs/2207.07958}, volume = {abs/2207.07958}, - year = {2022} + year = {2022}, } @article{eshraghian2023training, @@ -234,7 +263,8 @@ @article{eshraghian2023training title = {Training Spiking Neural Networks Using Lessons From Deep Learning}, url = {https://doi.org/10.1109/jproc.2023.3308088}, volume = {111}, - year = {2023} + year = {2023}, + month = sep, } @article{farah2005neuroethics, @@ -249,7 +279,8 @@ @article{farah2005neuroethics title = {Neuroethics: {The} practical and the philosophical}, url = {https://doi.org/10.1016/j.tics.2004.12.001}, volume = {9}, - year = {2005} + year = {2005}, + month = jan, } @inproceedings{fowers2018configurable, @@ -262,7 +293,8 @@ @inproceedings{fowers2018configurable source = {Crossref}, title = {A Configurable Cloud-Scale {DNN} Processor for Real-Time {AI}}, url = {https://doi.org/10.1109/isca.2018.00012}, - year = {2018} + year = {2018}, + month = jun, } @article{furber2016large, @@ -277,7 +309,8 @@ @article{furber2016large title = {Large-scale neuromorphic computing systems}, url = {https://doi.org/10.1088/1741-2560/13/5/051001}, volume = {13}, - year = {2016} + year = {2016}, + month = aug, } @article{gale2019state, @@ -286,7 +319,7 @@ @article{gale2019state title = {The state of sparsity in deep neural networks}, url = {https://arxiv.org/abs/1902.09574}, volume = {abs/1902.09574}, - year = {2019} + year = {2019}, } @inproceedings{gannot1994verilog, @@ -301,7 +334,7 @@ @inproceedings{gannot1994verilog title = {Verilog {HDL} based {FPGA} design}, url = {https://doi.org/10.1109/ivc.1994.323743}, volume = {}, - year = {1994} + year = {1994}, } @article{gates2009flexible, @@ -316,7 +349,8 @@ @article{gates2009flexible title = {Flexible Electronics}, url = {https://doi.org/10.1126/science.1171230}, volume = {323}, - year = {2009} + year = {2009}, + month = mar, } @article{goodyear2017social, @@ -331,13 +365,20 @@ @article{goodyear2017social title = {Social media, apps and wearable technologies: {Navigating} ethical dilemmas and procedures}, url = {https://doi.org/10.1080/2159676x.2017.1303790}, volume = {9}, - year = {2017} + year = {2017}, + month = mar, +} + +@article{gwennap_certus-nx_nodate, + author = {Gwennap, Linley}, + language = {en}, + title = {Certus-{NX} Innovates General-Purpose {FPGAs}}, } @article{gwennapcertusnx, author = {Gwennap, Linley}, language = {en}, - title = {Certus-{NX} Innovates General-Purpose {FPGAs}} + title = {Certus-{NX} Innovates General-Purpose {FPGAs}}, } @article{haensch2018next, @@ -352,7 +393,8 @@ @article{haensch2018next title = {The Next Generation of Deep Learning Hardware: {Analog} Computing}, url = {https://doi.org/10.1109/jproc.2018.2871057}, volume = {107}, - year = {2019} + year = {2019}, + month = jan, } @article{hazan2021neuromorphic, @@ -366,12 +408,13 @@ @article{hazan2021neuromorphic title = {Neuromorphic Analog Implementation of Neural Engineering Framework-Inspired Spiking Neuron for High-Dimensional Representation}, url = {https://doi.org/10.3389/fnins.2021.627221}, volume = {15}, - year = {2021} + year = {2021}, + month = feb, } @article{hennessy2019golden, - abstract = {Innovations like domain-specific hardware, enhanced security, open instruction sets, and agile chip development will lead the way.}, author = {Hennessy, John L. and Patterson, David A.}, + abstract = {Innovations like domain-specific hardware, enhanced security, open instruction sets, and agile chip development will lead the way.}, copyright = {http://www.acm.org/publications/policies/copyright\_policy\#Background}, doi = {10.1145/3282307}, issn = {0001-0782, 1557-7317}, @@ -384,7 +427,8 @@ @article{hennessy2019golden title = {A new golden age for computer architecture}, url = {https://doi.org/10.1145/3282307}, volume = {62}, - year = {2019} + year = {2019}, + month = jan, } @misc{howard2017mobilenets, @@ -393,7 +437,7 @@ @misc{howard2017mobilenets title = {{MobileNets:} {Efficient} Convolutional Neural Networks for Mobile Vision Applications}, url = {https://arxiv.org/abs/1704.04861}, volume = {abs/1704.04861}, - year = {2017} + year = {2017}, } @article{huang2010pseudo, @@ -408,56 +452,63 @@ @article{huang2010pseudo title = {Pseudo-{CMOS:} {A} Design Style for Low-Cost and Robust Flexible Electronics}, url = {https://doi.org/10.1109/ted.2010.2088127}, volume = {58}, - year = {2011} + year = {2011}, + month = jan, } -@inproceedings{ignatov2018ai, - author = {Ignatov, Andrey and Timofte, Radu and Kulik, Andrei and Yang, Seungsoo and Wang, Ke and Baum, Felix and Wu, Max and Xu, Lirong and Van Gool, Luc}, - booktitle = {2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)}, - doi = {10.1109/iccvw.2019.00447}, - pages = {0--0}, - publisher = {IEEE}, +@article{huang2022flexible, + author = {Huang, Shihua and Waeijen, Luc and Corporaal, Henk}, + title = {How Flexible is Your Computing System?}, + journal = {ACM Trans. Embedded Comput. Syst.}, + volume = {21}, + number = {4}, + pages = {1--41}, + year = {2022}, + publisher = {Association for Computing Machinery (ACM)}, + doi = {10.1145/3524861}, source = {Crossref}, - title = {{AI} Benchmark: {All} About Deep Learning on Smartphones in 2019}, - url = {https://doi.org/10.1109/iccvw.2019.00447}, - year = {2019} + url = {https://doi.org/10.1145/3524861}, + issn = {1539-9087, 1558-3465}, + month = jul, } @article{ignatov2018ai, - abstract = {Over the last years, the computational power of mobile devices such as smartphones and tablets has grown dramatically, reaching the level of desktop computers available not long ago. While standard smartphone apps are no longer a problem for them, there is still a group of tasks that can easily challenge even high-end devices, namely running artificial intelligence algorithms. In this paper, we present a study of the current state of deep learning in the Android ecosystem and describe available frameworks, programming models and the limitations of running AI on smartphones. We give an overview of the hardware acceleration resources available on four main mobile chipset platforms: Qualcomm, HiSilicon, MediaTek and Samsung. Additionally, we present the real-world performance results of different mobile SoCs collected with AI Benchmark that are covering all main existing hardware configurations.}, author = {Ignatov, Andrey and Timofte, Radu and Chou, William and Wang, Ke and Wu, Max and Hartley, Tim and Van Gool, Luc}, + abstract = {Over the last years, the computational power of mobile devices such as smartphones and tablets has grown dramatically, reaching the level of desktop computers available not long ago. While standard smartphone apps are no longer a problem for them, there is still a group of tasks that can easily challenge even high-end devices, namely running artificial intelligence algorithms. In this paper, we present a study of the current state of deep learning in the Android ecosystem and describe available frameworks, programming models and the limitations of running AI on smartphones. We give an overview of the hardware acceleration resources available on four main mobile chipset platforms: Qualcomm, HiSilicon, MediaTek and Samsung. Additionally, we present the real-world performance results of different mobile SoCs collected with AI Benchmark that are covering all main existing hardware configurations.}, booktitle = {Proceedings of the European Conference on Computer Vision (ECCV) Workshops}, pages = {0--0}, publisher = {arXiv}, title = {{AI} Benchmark: {Running} deep neural networks on Android smartphones}, - year = {2018} + year = {2018}, } @inproceedings{imani2016resistive, author = {Imani, Mohsen and Rahimi, Abbas and S. Rosing, Tajana}, booktitle = {Proceedings of the 2016 Design, Automation \& Test in Europe Conference \& Exhibition (DATE)}, - doi = {10.3850/9783981537079\_0454}, + doi = {10.3850/9783981537079_0454}, organization = {IEEE}, pages = {1327--1332}, publisher = {Research Publishing Services}, source = {Crossref}, title = {Resistive Configurable Associative Memory for Approximate Computing}, - url = {https://doi.org/10.3850/9783981537079\_0454}, - year = {2016} + url = {https://doi.org/10.3850/9783981537079_0454}, + year = {2016}, } @inproceedings{jacob2018quantization, - author = {Benoit Jacob and Skirmantas Kligys and Bo Chen and Menglong Zhu and Matthew Tang and Andrew G. Howard and Hartwig Adam and Dmitry Kalenichenko}, + author = {Jacob, Benoit and Kligys, Skirmantas and Chen, Bo and Zhu, Menglong and Tang, Matthew and Howard, Andrew and Adam, Hartwig and Kalenichenko, Dmitry}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/cvpr/JacobKCZTHAK18.bib}, - booktitle = {2018 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2018, Salt Lake City, UT, USA, June 18-22, 2018}, - doi = {10.1109/CVPR.2018.00286}, + booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition}, + doi = {10.1109/cvpr.2018.00286}, pages = {2704--2713}, - publisher = {{IEEE} Computer Society}, + publisher = {IEEE}, timestamp = {Wed, 06 Feb 2019 00:00:00 +0100}, title = {Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference}, - url = {http://openaccess.thecvf.com/content\_cvpr\_2018/html/Jacob\_Quantization\_and\_Training\_CVPR\_2018\_paper.html}, - year = {2018} + url = {https://doi.org/10.1109/cvpr.2018.00286}, + year = {2018}, + source = {Crossref}, + month = jun, } @misc{jia2018dissecting, @@ -466,26 +517,26 @@ @misc{jia2018dissecting title = {Dissecting the {NVIDIA} {Volta} {GPU} Architecture via Microbenchmarking}, url = {https://arxiv.org/abs/1804.06826}, volume = {abs/1804.06826}, - year = {2018} + year = {2018}, } @inproceedings{jia2019beyond, - author = {Zhihao Jia and Matei Zaharia and Alex Aiken}, + author = {Jia, Zhihao and Zaharia, Matei and Aiken, Alex}, + editor = {Talwalkar, Ameet and Smith, Virginia and Zaharia, Matei}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/mlsys/JiaZA19.bib}, booktitle = {Proceedings of Machine Learning and Systems 2019, MLSys 2019, Stanford, CA, USA, March 31 - April 2, 2019}, - editor = {Ameet Talwalkar and Virginia Smith and Matei Zaharia}, publisher = {mlsys.org}, timestamp = {Thu, 18 Jun 2020 01:00:00 +0200}, title = {Beyond Data and Model Parallelism for Deep Neural Networks}, url = {https://proceedings.mlsys.org/book/265.pdf}, - year = {2019} + year = {2019}, } @inproceedings{jouppi2017datacenter, + author = {Jouppi, Norman P. and Young, Cliff and Patil, Nishant and Patterson, David and Agrawal, Gaurav and Bajwa, Raminder and Bates, Sarah and Bhatia, Suresh and Boden, Nan and Borchers, Al and Boyle, Rick and Cantin, Pierre-luc and Chao, Clifford and Clark, Chris and Coriell, Jeremy and Daley, Mike and Dau, Matt and Dean, Jeffrey and Gelb, Ben and Ghaemmaghami, Tara Vazir and Gottipati, Rajendra and Gulland, William and Hagmann, Robert and Ho, C. Richard and Hogberg, Doug and Hu, John and Hundt, Robert and Hurt, Dan and Ibarz, Julian and Jaffey, Aaron and Jaworski, Alek and Kaplan, Alexander and Khaitan, Harshit and Killebrew, Daniel and Koch, Andy and Kumar, Naveen and Lacy, Steve and Laudon, James and Law, James and Le, Diemthu and Leary, Chris and Liu, Zhuyuan and Lucke, Kyle and Lundin, Alan and MacKean, Gordon and Maggiore, Adriana and Mahony, Maire and Miller, Kieran and Nagarajan, Rahul and Narayanaswami, Ravi and Ni, Ray and Nix, Kathy and Norrie, Thomas and Omernick, Mark and Penukonda, Narayana and Phelps, Andy and Ross, Jonathan and Ross, Matt and Salek, Amir and Samadiani, Emad and Severn, Chris and Sizikov, Gregory and Snelham, Matthew and Souter, Jed and Steinberg, Dan and Swing, Andy and Tan, Mercedes and Thorson, Gregory and Tian, Bo and Toma, Horia and Tuttle, Erick and Vasudevan, Vijay and Walter, Richard and Wang, Walter and Wilcox, Eric and Yoon, Doe Hyun}, abstract = {Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC{\textemdash}called a Tensor Processing Unit (TPU) {\textemdash} deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95\% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X {\textendash} 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X {\textendash} 80X higher. Moreover, using the CPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.}, address = {New York, NY, USA}, - author = {Jouppi, Norman P. and Young, Cliff and Patil, Nishant and Patterson, David and Agrawal, Gaurav and Bajwa, Raminder and Bates, Sarah and Bhatia, Suresh and Boden, Nan and Borchers, Al and Boyle, Rick and Cantin, Pierre-luc and Chao, Clifford and Clark, Chris and Coriell, Jeremy and Daley, Mike and Dau, Matt and Dean, Jeffrey and Gelb, Ben and Ghaemmaghami, Tara Vazir and Gottipati, Rajendra and Gulland, William and Hagmann, Robert and Ho, C. Richard and Hogberg, Doug and Hu, John and Hundt, Robert and Hurt, Dan and Ibarz, Julian and Jaffey, Aaron and Jaworski, Alek and Kaplan, Alexander and Khaitan, Harshit and Killebrew, Daniel and Koch, Andy and Kumar, Naveen and Lacy, Steve and Laudon, James and Law, James and Le, Diemthu and Leary, Chris and Liu, Zhuyuan and Lucke, Kyle and Lundin, Alan and MacKean, Gordon and Maggiore, Adriana and Mahony, Maire and Miller, Kieran and Nagarajan, Rahul and Narayanaswami, Ravi and Ni, Ray and Nix, Kathy and Norrie, Thomas and Omernick, Mark and Penukonda, Narayana and Phelps, Andy and Ross, Jonathan and Ross, Matt and Salek, Amir and Samadiani, Emad and Severn, Chris and Sizikov, Gregory and Snelham, Matthew and Souter, Jed and Steinberg, Dan and Swing, Andy and Tan, Mercedes and Thorson, Gregory and Tian, Bo and Toma, Horia and Tuttle, Erick and Vasudevan, Vijay and Walter, Richard and Wang, Walter and Wilcox, Eric and Yoon, Doe Hyun}, bdsk-url-1 = {https://doi.org/10.1145/3079856.3080246}, booktitle = {Proceedings of the 44th Annual International Symposium on Computer Architecture}, doi = {10.1145/3079856.3080246}, @@ -499,13 +550,14 @@ @inproceedings{jouppi2017datacenter source = {Crossref}, title = {In-Datacenter Performance Analysis of a Tensor Processing Unit}, url = {https://doi.org/10.1145/3079856.3080246}, - year = {2017} + year = {2017}, + month = jun, } @inproceedings{jouppi2017indatacenter, + author = {Jouppi, Norman P. and Young, Cliff and Patil, Nishant and Patterson, David and Agrawal, Gaurav and Bajwa, Raminder and Bates, Sarah and Bhatia, Suresh and Boden, Nan and Borchers, Al and Boyle, Rick and Cantin, Pierre-luc and Chao, Clifford and Clark, Chris and Coriell, Jeremy and Daley, Mike and Dau, Matt and Dean, Jeffrey and Gelb, Ben and Ghaemmaghami, Tara Vazir and Gottipati, Rajendra and Gulland, William and Hagmann, Robert and Ho, C. Richard and Hogberg, Doug and Hu, John and Hundt, Robert and Hurt, Dan and Ibarz, Julian and Jaffey, Aaron and Jaworski, Alek and Kaplan, Alexander and Khaitan, Harshit and Killebrew, Daniel and Koch, Andy and Kumar, Naveen and Lacy, Steve and Laudon, James and Law, James and Le, Diemthu and Leary, Chris and Liu, Zhuyuan and Lucke, Kyle and Lundin, Alan and MacKean, Gordon and Maggiore, Adriana and Mahony, Maire and Miller, Kieran and Nagarajan, Rahul and Narayanaswami, Ravi and Ni, Ray and Nix, Kathy and Norrie, Thomas and Omernick, Mark and Penukonda, Narayana and Phelps, Andy and Ross, Jonathan and Ross, Matt and Salek, Amir and Samadiani, Emad and Severn, Chris and Sizikov, Gregory and Snelham, Matthew and Souter, Jed and Steinberg, Dan and Swing, Andy and Tan, Mercedes and Thorson, Gregory and Tian, Bo and Toma, Horia and Tuttle, Erick and Vasudevan, Vijay and Walter, Richard and Wang, Walter and Wilcox, Eric and Yoon, Doe Hyun}, abstract = {Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC{\textemdash}called a Tensor Processing Unit (TPU) {\textemdash} deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95\% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X {\textendash} 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X {\textendash} 80X higher. Moreover, using the CPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.}, address = {New York, NY, USA}, - author = {Jouppi, Norman P. and Young, Cliff and Patil, Nishant and Patterson, David and Agrawal, Gaurav and Bajwa, Raminder and Bates, Sarah and Bhatia, Suresh and Boden, Nan and Borchers, Al and Boyle, Rick and Cantin, Pierre-luc and Chao, Clifford and Clark, Chris and Coriell, Jeremy and Daley, Mike and Dau, Matt and Dean, Jeffrey and Gelb, Ben and Ghaemmaghami, Tara Vazir and Gottipati, Rajendra and Gulland, William and Hagmann, Robert and Ho, C. Richard and Hogberg, Doug and Hu, John and Hundt, Robert and Hurt, Dan and Ibarz, Julian and Jaffey, Aaron and Jaworski, Alek and Kaplan, Alexander and Khaitan, Harshit and Killebrew, Daniel and Koch, Andy and Kumar, Naveen and Lacy, Steve and Laudon, James and Law, James and Le, Diemthu and Leary, Chris and Liu, Zhuyuan and Lucke, Kyle and Lundin, Alan and MacKean, Gordon and Maggiore, Adriana and Mahony, Maire and Miller, Kieran and Nagarajan, Rahul and Narayanaswami, Ravi and Ni, Ray and Nix, Kathy and Norrie, Thomas and Omernick, Mark and Penukonda, Narayana and Phelps, Andy and Ross, Jonathan and Ross, Matt and Salek, Amir and Samadiani, Emad and Severn, Chris and Sizikov, Gregory and Snelham, Matthew and Souter, Jed and Steinberg, Dan and Swing, Andy and Tan, Mercedes and Thorson, Gregory and Tian, Bo and Toma, Horia and Tuttle, Erick and Vasudevan, Vijay and Walter, Richard and Wang, Walter and Wilcox, Eric and Yoon, Doe Hyun}, bdsk-url-1 = {https://doi.org/10.1145/3079856.3080246}, booktitle = {Proceedings of the 44th Annual International Symposium on Computer Architecture}, doi = {10.1145/3079856.3080246}, @@ -519,14 +571,15 @@ @inproceedings{jouppi2017indatacenter source = {Crossref}, title = {In-Datacenter Performance Analysis of a Tensor Processing Unit}, url = {https://doi.org/10.1145/3079856.3080246}, - year = {2017} + year = {2017}, + month = jun, } @inproceedings{jouppi2023tpu, + author = {Jouppi, Norm and Kurian, George and Li, Sheng and Ma, Peter and Nagarajan, Rahul and Nai, Lifeng and Patil, Nishant and Subramanian, Suvinay and Swing, Andy and Towles, Brian and Young, Clifford and Zhou, Xiang and Zhou, Zongwei and Patterson, David A}, abstract = {In response to innovations in machine learning (ML) models, production workloads changed radically and rapidly. TPU v4 is the fifth Google domain specific architecture (DSA) and its third supercomputer for such ML models. Optical circuit switches (OCSes) dynamically reconfigure its interconnect topology to improve scale, availability, utilization, modularity, deployment, security, power, and performance; users can pick a twisted 3D torus topology if desired. Much cheaper, lower power, and faster than Infiniband, OCSes and underlying optical components are lt;5\% of system cost and lt;3\% of system power. Each TPU v4 includes SparseCores, dataflow processors that accelerate models that rely on embeddings by 5x{\textendash}7x yet use only 5\% of die area and power. Deployed since 2020, TPU v4 outperforms TPU v3 by 2.1x and improves performance/Watt by 2.7x. The TPU v4 supercomputer is 4x larger at 4096 chips and thus nearly 10x faster overall, which along with OCS flexibility and availability allows a large language model to train at an average of ~60\% of peak FLOPS/second. For similar sized systems, it is ~4.3x{\textendash}4.5x faster than the Graphcore IPU Bow and is 1.2x{\textendash}1.7x faster and uses 1.3x{\textendash}1.9x less power than the Nvidia A100. TPU v4s inside the energy-optimized warehouse scale computers of Google Cloud use ~2{\textendash}6x less energy and produce ~20x less CO2e than contemporary DSAs in typical on-premise data centers.}, address = {New York, NY, USA}, articleno = {82}, - author = {Jouppi, Norm and Kurian, George and Li, Sheng and Ma, Peter and Nagarajan, Rahul and Nai, Lifeng and Patil, Nishant and Subramanian, Suvinay and Swing, Andy and Towles, Brian and Young, Clifford and Zhou, Xiang and Zhou, Zongwei and Patterson, David A}, bdsk-url-1 = {https://doi.org/10.1145/3579371.3589350}, booktitle = {Proceedings of the 50th Annual International Symposium on Computer Architecture}, doi = {10.1145/3579371.3589350}, @@ -539,7 +592,8 @@ @inproceedings{jouppi2023tpu source = {Crossref}, title = {{TPU} v4: {An} Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings}, url = {https://doi.org/10.1145/3579371.3589350}, - year = {2023} + year = {2023}, + month = jun, } @inproceedings{kao2020confuciux, @@ -547,25 +601,36 @@ @inproceedings{kao2020confuciux booktitle = {2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)}, organization = {IEEE}, pages = {622--636}, - title = {Confuciux: Autonomous hardware resource assignment for dnn accelerators using reinforcement learning}, - year = {2020} + title = {{ConfuciuX:} {Autonomous} Hardware Resource Assignment for {DNN} Accelerators using Reinforcement Learning}, + year = {2020}, + doi = {10.1109/micro50266.2020.00058}, + source = {Crossref}, + url = {https://doi.org/10.1109/micro50266.2020.00058}, + publisher = {IEEE}, + month = oct, } @inproceedings{kao2020gamma, author = {Kao, Sheng-Chun and Krishna, Tushar}, booktitle = {Proceedings of the 39th International Conference on Computer-Aided Design}, pages = {1--9}, - title = {Gamma: Automating the hw mapping of dnn models on accelerators via genetic algorithm}, - year = {2020} + title = {Gamma}, + year = {2020}, + doi = {10.1145/3400302.3415639}, + source = {Crossref}, + url = {https://doi.org/10.1145/3400302.3415639}, + publisher = {ACM}, + subtitle = {automating the HW mapping of DNN models on accelerators via genetic algorithm}, + month = nov, } @misc{krishnan2022multiagent, + author = {Krishnan, Srivatsan and Jaques, Natasha and Omidshafiei, Shayegan and Zhang, Dan and Gur, Izzeddin and Reddi, Vijay Janapa and Faust, Aleksandra}, archiveprefix = {arXiv}, - author = {Srivatsan Krishnan and Natasha Jaques and Shayegan Omidshafiei and Dan Zhang and Izzeddin Gur and Vijay Janapa Reddi and Aleksandra Faust}, eprint = {2211.16385}, primaryclass = {cs.AR}, title = {Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration}, - year = {2022} + year = {2022}, } @inproceedings{krishnan2023archgym, @@ -577,7 +642,8 @@ @inproceedings{krishnan2023archgym source = {Crossref}, title = {{ArchGym:} {An} Open-Source Gymnasium for Machine Learning Assisted Architecture Design}, url = {https://doi.org/10.1145/3579371.3589049}, - year = {2023} + year = {2023}, + month = jun, } @article{kwon2022flexible, @@ -591,19 +657,8 @@ @article{kwon2022flexible title = {Flexible sensors and machine learning for heart monitoring}, url = {https://doi.org/10.1016/j.nanoen.2022.107632}, volume = {102}, - year = {2022} -} - -@inproceedings{Li2020Additive, - author = {Yuhang Li and Xin Dong and Wei Wang}, - bibsource = {dblp computer science bibliography, https://dblp.org}, - biburl = {https://dblp.org/rec/conf/iclr/LiDW20.bib}, - booktitle = {8th International Conference on Learning Representations, {ICLR} 2020, Addis Ababa, Ethiopia, April 26-30, 2020}, - publisher = {OpenReview.net}, - timestamp = {Tue, 18 Aug 2020 01:00:00 +0200}, - title = {Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks}, - url = {https://openreview.net/forum?id=BkgXT24tDS}, - year = {2020} + year = {2022}, + month = nov, } @inproceedings{lin2022ondevice, @@ -614,19 +669,20 @@ @inproceedings{lin2022ondevice source = {Crossref}, title = {{PockEngine:} {Sparse} and Efficient Fine-tuning in a Pocket}, url = {https://doi.org/10.1145/3613424.3614307}, - year = {2023} + year = {2023}, + month = oct, } @article{lin2023awq, author = {Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song}, journal = {arXiv}, title = {{AWQ:} {Activation-aware} Weight Quantization for {LLM} Compression and Acceleration}, - year = {2023} + year = {2023}, } @article{lindholm2008nvidia, - abstract = {To enable flexible, programmable graphics and high-performance computing, NVIDIA has developed the Tesla scalable unified graphics and parallel computing architecture. Its scalable parallel array of processors is massively multithreaded and programmable in C or via graphics APIs.}, author = {Lindholm, Erik and Nickolls, John and Oberman, Stuart and Montrym, John}, + abstract = {To enable flexible, programmable graphics and high-performance computing, NVIDIA has developed the Tesla scalable unified graphics and parallel computing architecture. Its scalable parallel array of processors is massively multithreaded and programmable in C or via graphics APIs.}, bdsk-url-1 = {https://ieeexplore.ieee.org/document/4523358}, bdsk-url-2 = {https://doi.org/10.1109/MM.2008.31}, doi = {10.1109/mm.2008.31}, @@ -642,7 +698,8 @@ @article{lindholm2008nvidia url = {https://doi.org/10.1109/mm.2008.31}, urldate = {2023-11-07}, volume = {28}, - year = {2008} + year = {2008}, + month = mar, } @article{loh20083dstacked, @@ -657,7 +714,8 @@ @article{loh20083dstacked title = {{3D}-Stacked Memory Architectures for Multi-core Processors}, url = {https://doi.org/10.1145/1394608.1382159}, volume = {36}, - year = {2008} + year = {2008}, + month = jun, } @inproceedings{luebke2008cuda, @@ -672,7 +730,8 @@ @inproceedings{luebke2008cuda title = {{CUDA:} {Scalable} parallel programming for high-performance scientific computing}, url = {https://doi.org/10.1109/isbi.2008.4541126}, volume = {}, - year = {2008} + year = {2008}, + month = may, } @article{maass1997networks, @@ -687,7 +746,8 @@ @article{maass1997networks title = {Networks of spiking neurons: {The} third generation of neural network models}, url = {https://doi.org/10.1016/s0893-6080(97)00011-7}, volume = {10}, - year = {1997} + year = {1997}, + month = dec, } @article{markovic2020physics, @@ -702,7 +762,8 @@ @article{markovic2020physics title = {Physics for neuromorphic computing}, url = {https://doi.org/10.1038/s42254-020-0208-2}, volume = {2}, - year = {2020} + year = {2020}, + month = jul, } @article{mattson2020mlperf, @@ -717,7 +778,8 @@ @article{mattson2020mlperf title = {{MLPerf:} {An} Industry Standard Benchmark Suite for Machine Learning Performance}, url = {https://doi.org/10.1109/mm.2020.2974843}, volume = {40}, - year = {2020} + year = {2020}, + month = mar, } @article{miller2000optical, @@ -732,18 +794,24 @@ @article{miller2000optical title = {Optical interconnects to silicon}, url = {https://doi.org/10.1109/2944.902184}, volume = {6}, - year = {2000} + year = {2000}, + month = nov, } @article{mirhoseini2021graph, - author = {Mirhoseini, Azalia and Goldie, Anna and Yazgan, Mustafa and Jiang, Joe Wenjie and Songhori, Ebrahim and Wang, Shen and Lee, Young-Joon and Johnson, Eric and Pathak, Omkar and Nazi, Azade and others}, + author = {Mirhoseini, Azalia and Goldie, Anna and Yazgan, Mustafa and Jiang, Joe Wenjie and Songhori, Ebrahim and Wang, Shen and Lee, Young-Joon and Johnson, Eric and Pathak, Omkar and Nazi, Azade and Pak, Jiwoo and Tong, Andy and Srinivasa, Kavya and Hang, William and Tuncer, Emre and Le, Quoc V. and Laudon, James and Ho, Richard and Carpenter, Roger and Dean, Jeff}, journal = {Nature}, number = {7862}, pages = {207--212}, - publisher = {Nature Publishing Group}, + publisher = {Springer Science and Business Media LLC}, title = {A graph placement methodology for fast chip design}, volume = {594}, - year = {2021} + year = {2021}, + doi = {10.1038/s41586-021-03544-w}, + source = {Crossref}, + url = {https://doi.org/10.1038/s41586-021-03544-w}, + issn = {0028-0836, 1476-4687}, + month = jun, } @article{mittal2021survey, @@ -757,7 +825,8 @@ @article{mittal2021survey title = {A survey of {SRAM}-based in-memory computing techniques and applications}, url = {https://doi.org/10.1016/j.sysarc.2021.102276}, volume = {119}, - year = {2021} + year = {2021}, + month = oct, } @article{modha2023neural, @@ -772,7 +841,8 @@ @article{modha2023neural title = {Neural inference at the frontier of energy, space, and time}, url = {https://doi.org/10.1126/science.adh1174}, volume = {382}, - year = {2023} + year = {2023}, + month = oct, } @inproceedings{munshi2009opencl, @@ -787,7 +857,8 @@ @inproceedings{munshi2009opencl title = {The {OpenCL} specification}, url = {https://doi.org/10.1109/hotchips.2009.7478342}, volume = {}, - year = {2009} + year = {2009}, + month = aug, } @article{musk2019integrated, @@ -802,7 +873,8 @@ @article{musk2019integrated title = {An Integrated Brain-Machine Interface Platform With Thousands of Channels}, url = {https://doi.org/10.2196/16194}, volume = {21}, - year = {2019} + year = {2019}, + month = oct, } @article{norrie2021design, @@ -818,19 +890,20 @@ @article{norrie2021design title = {The Design Process for Google's Training Chips: {Tpuv2} and {TPUv3}}, url = {https://doi.org/10.1109/mm.2021.3058217}, volume = {41}, - year = {2021} + year = {2021}, + month = mar, } @book{patterson2016computer, author = {Patterson, David A and Hennessy, John L}, publisher = {Morgan kaufmann}, title = {Computer organization and design {ARM} edition: {The} hardware software interface}, - year = {2016} + year = {2016}, } @article{putnam2014reconfigurable, - abstract = {Datacenter workloads demand high computational capabilities, flexibility, power efficiency, and low cost. It is challenging to improve all of these factors simultaneously. To advance datacenter capabilities beyond what commodity server designs can provide, we have designed and built a composable, reconfigurablefabric to accelerate portions of large-scale software services. Each instantiation of the fabric consists of a 6x8 2-D torus of high-end Stratix V FPGAs embedded into a half-rack of 48 machines. One FPGA is placed into each server, accessible through PCIe, and wired directly to other FPGAs with pairs of 10 Gb SAS cables In this paper, we describe a medium-scale deployment of this fabric on a bed of 1,632 servers, and measure its efficacy in accelerating the Bing web search engine. We describe the requirements and architecture of the system, detail the critical engineering challenges and solutions needed to make the system robust in the presence of failures, and measure the performance, power, and resilience of the system when ranking candidate documents. Under high load, the largescale reconfigurable fabric improves the ranking throughput of each server by a factor of 95\% for a fixed latency distribution{\textemdash} or, while maintaining equivalent throughput, reduces the tail latency by 29\%}, author = {Putnam, Andrew and Caulfield, Adrian M. and Chung, Eric S. and Chiou, Derek and Constantinides, Kypros and Demme, John and Esmaeilzadeh, Hadi and Fowers, Jeremy and Gopal, Gopi Prashanth and Gray, Jan and Haselman, Michael and Hauck, Scott and Heil, Stephen and Hormati, Amir and Kim, Joo-Young and Lanka, Sitaram and Larus, James and Peterson, Eric and Pope, Simon and Smith, Aaron and Thong, Jason and Xiao, Phillip Yi and Burger, Doug}, + abstract = {Datacenter workloads demand high computational capabilities, flexibility, power efficiency, and low cost. It is challenging to improve all of these factors simultaneously. To advance datacenter capabilities beyond what commodity server designs can provide, we have designed and built a composable, reconfigurablefabric to accelerate portions of large-scale software services. Each instantiation of the fabric consists of a 6x8 2-D torus of high-end Stratix V FPGAs embedded into a half-rack of 48 machines. One FPGA is placed into each server, accessible through PCIe, and wired directly to other FPGAs with pairs of 10 Gb SAS cables In this paper, we describe a medium-scale deployment of this fabric on a bed of 1,632 servers, and measure its efficacy in accelerating the Bing web search engine. We describe the requirements and architecture of the system, detail the critical engineering challenges and solutions needed to make the system robust in the presence of failures, and measure the performance, power, and resilience of the system when ranking candidate documents. Under high load, the largescale reconfigurable fabric improves the ranking throughput of each server by a factor of 95\% for a fixed latency distribution{\textemdash} or, while maintaining equivalent throughput, reduces the tail latency by 29\%}, bdsk-url-1 = {https://dl.acm.org/doi/10.1145/2678373.2665678}, bdsk-url-2 = {https://doi.org/10.1145/2678373.2665678}, doi = {10.1145/2678373.2665678}, @@ -845,24 +918,27 @@ @article{putnam2014reconfigurable url = {https://doi.org/10.1145/2678373.2665678}, urldate = {2023-11-07}, volume = {42}, - year = {2014} + year = {2014}, + month = jun, } @inproceedings{rajat2009largescale, - author = {Rajat Raina and Anand Madhavan and Andrew Y. Ng}, + author = {Raina, Rajat and Madhavan, Anand and Ng, Andrew Y.}, + editor = {Danyluk, Andrea Pohoreckyj and Bottou, L\'eon and Littman, Michael L.}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/icml/RainaMN09.bib}, - booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning, {ICML} 2009, Montreal, Quebec, Canada, June 14-18, 2009}, + booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning}, doi = {10.1145/1553374.1553486}, - editor = {Andrea Pohoreckyj Danyluk and L{\'{e}}on Bottou and Michael L. Littman}, pages = {873--880}, - publisher = {{ACM}}, - series = {{ACM} International Conference Proceeding Series}, + publisher = {ACM}, + series = {ACM International Conference Proceeding Series}, timestamp = {Wed, 14 Nov 2018 00:00:00 +0100}, title = {Large-scale deep unsupervised learning using graphics processors}, url = {https://doi.org/10.1145/1553374.1553486}, volume = {382}, - year = {2009} + year = {2009}, + source = {Crossref}, + month = jun, } @article{ranganathan2011from, @@ -877,16 +953,22 @@ @article{ranganathan2011from title = {From Microprocessors to Nanostores: {Rethinking} Data-Centric Systems}, url = {https://doi.org/10.1109/mc.2011.18}, volume = {44}, - year = {2011} + year = {2011}, + month = jan, } @inproceedings{reagen2017case, - author = {Reagen, Brandon and Hern{\'a}ndez-Lobato, Jos{\'e} Miguel and Adolf, Robert and Gelbart, Michael and Whatmough, Paul and Wei, Gu-Yeon and Brooks, David}, + author = {Reagen, Brandon and Hernandez-Lobato, Jose Miguel and Adolf, Robert and Gelbart, Michael and Whatmough, Paul and Wei, Gu-Yeon and Brooks, David}, booktitle = {2017 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)}, organization = {IEEE}, pages = {1--6}, - title = {A case for efficient accelerator design space exploration via bayesian optimization}, - year = {2017} + title = {A case for efficient accelerator design space exploration via {Bayesian} optimization}, + year = {2017}, + doi = {10.1109/islped.2017.8009208}, + source = {Crossref}, + url = {https://doi.org/10.1109/islped.2017.8009208}, + publisher = {IEEE}, + month = jul, } @inproceedings{reddi2020mlperf, @@ -899,7 +981,8 @@ @inproceedings{reddi2020mlperf source = {Crossref}, title = {{MLPerf} Inference Benchmark}, url = {https://doi.org/10.1109/isca45697.2020.00045}, - year = {2020} + year = {2020}, + month = may, } @article{roskies2002neuroethics, @@ -914,7 +997,8 @@ @article{roskies2002neuroethics title = {Neuroethics for the New Millenium}, url = {https://doi.org/10.1016/s0896-6273(02)00763-8}, volume = {35}, - year = {2002} + year = {2002}, + month = jul, } @article{samajdar2018scale, @@ -923,7 +1007,7 @@ @article{samajdar2018scale title = {Scale-sim: {Systolic} cnn accelerator simulator}, url = {https://arxiv.org/abs/1811.02883}, volume = {abs/1811.02883}, - year = {2018} + year = {2018}, } @article{schuman2022opportunities, @@ -938,13 +1022,14 @@ @article{schuman2022opportunities title = {Opportunities for neuromorphic computing algorithms and applications}, url = {https://doi.org/10.1038/s43588-021-00184-y}, volume = {2}, - year = {2022} + year = {2022}, + month = jan, } @misc{segal1999opengl, author = {Segal, Mark and Akeley, Kurt}, title = {The {OpenGL} graphics system: {A} specification (version 1.1)}, - year = {1999} + year = {1999}, } @article{segura2018ethical, @@ -959,7 +1044,8 @@ @article{segura2018ethical title = {Ethical Implications of User Perceptions of Wearable Devices}, url = {https://doi.org/10.1007/s11948-017-9872-8}, volume = {24}, - year = {2017} + year = {2017}, + month = feb, } @article{shastri2021photonics, @@ -974,7 +1060,8 @@ @article{shastri2021photonics title = {Photonics for artificial intelligence and neuromorphic computing}, url = {https://doi.org/10.1038/s41566-020-00754-y}, volume = {15}, - year = {2021} + year = {2021}, + month = jan, } @inproceedings{suda2016throughput, @@ -986,28 +1073,14 @@ @inproceedings{suda2016throughput source = {Crossref}, title = {Throughput-Optimized {OpenCL}-based {FPGA} Accelerator for Large-Scale Convolutional Neural Networks}, url = {https://doi.org/10.1145/2847263.2847276}, - year = {2016} + year = {2016}, + month = feb, } @article{sze2017efficient, author = {Sze, Vivienne and Chen, Yu-Hsin and Yang, Tien-Ju and Emer, Joel S.}, - doi = {10.1109/jproc.2017.2761740}, - issn = {0018-9219, 1558-2256}, - journal = {Proc. IEEE}, - number = {12}, - pages = {2295--2329}, - publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, - source = {Crossref}, - title = {Efficient Processing of Deep Neural Networks: {A} Tutorial and Survey}, - url = {https://doi.org/10.1109/jproc.2017.2761740}, - volume = {105}, - year = {2017} -} - -@article{sze2017efficient, abstract = {Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI systems. This article aims to provide a comprehensive tutorial and survey about the recent advances towards the goal of enabling efficient processing of DNNs. Specifically, it will provide an overview of DNNs, discuss various hardware platforms and architectures that support DNNs, and highlight key trends in reducing the computation cost of DNNs either solely via hardware design changes or via joint hardware design and DNN algorithm changes. It will also summarize various development resources that enable researchers and practitioners to quickly get started in this field, and highlight important benchmarking metrics and design considerations that should be used for evaluating the rapidly growing number of DNN hardware designs, optionally including algorithmic co-designs, being proposed in academia and industry. The reader will take away the following concepts from this article: understand the key design considerations for DNNs; be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics; understand the trade-offs between various hardware architectures and platforms; be able to evaluate the utility of various DNN design techniques for efficient processing; and understand recent implementation trends and opportunities.}, archiveprefix = {arXiv}, - author = {Sze, Vivienne and Chen, Yu-Hsin and Yang, Tien-Ju and Emer, Joel S.}, copyright = {http://arxiv.org/licenses/nonexclusive-distrib/1.0/}, doi = {10.1109/jproc.2017.2761740}, eprint = {1703.09039}, @@ -1021,7 +1094,8 @@ @article{sze2017efficient title = {Efficient Processing of Deep Neural Networks: {A} Tutorial and Survey}, url = {https://doi.org/10.1109/jproc.2017.2761740}, volume = {105}, - year = {2017} + year = {2017}, + month = dec, } @article{tang2022soft, @@ -1035,7 +1109,8 @@ @article{tang2022soft title = {Soft bioelectronics for cardiac interfaces}, url = {https://doi.org/10.1063/5.0069516}, volume = {3}, - year = {2022} + year = {2022}, + month = jan, } @article{tang2023flexible, @@ -1050,16 +1125,17 @@ @article{tang2023flexible title = {Flexible brain{\textendash}computer interfaces}, url = {https://doi.org/10.1038/s41928-022-00913-9}, volume = {6}, - year = {2023} + year = {2023}, + month = feb, } @inproceedings{valenzuela2000genetic, author = {Valenzuela, Christine L and Wang, Pearl Y}, - booktitle = {Parallel Problem Solving from Nature PPSN VI: 6th International Conference Paris, France, September 18--20, 2000 Proceedings 6}, + booktitle = {Parallel Problem Solving from Nature PPSN VI: 6th International Conference Paris, France, September 18{\textendash}20, 2000 Proceedings 6}, organization = {Springer}, pages = {671--680}, - title = {A genetic algorithm for VLSI floorplanning}, - year = {2000} + title = {A genetic algorithm for {VLSI} floorplanning}, + year = {2000}, } @article{verma2019memory, @@ -1074,7 +1150,7 @@ @article{verma2019memory title = {In-Memory Computing: {Advances} and Prospects}, url = {https://doi.org/10.1109/mssc.2019.2922889}, volume = {11}, - year = {2019} + year = {2019}, } @article{vivet2021intact, @@ -1090,21 +1166,24 @@ @article{vivet2021intact title = {{IntAct:} {A} 96-Core Processor With Six Chiplets {3D}-Stacked on an Active Interposer With Distributed Interconnects and Integrated Power Management}, url = {https://doi.org/10.1109/jssc.2020.3036341}, volume = {56}, - year = {2021} + year = {2021}, + month = jan, } @inproceedings{wang2020apq, - author = {Tianzhe Wang and Kuan Wang and Han Cai and Ji Lin and Zhijian Liu and Hanrui Wang and Yujun Lin and Song Han}, + author = {Wang, Tianzhe and Wang, Kuan and Cai, Han and Lin, Ji and Liu, Zhijian and Wang, Hanrui and Lin, Yujun and Han, Song}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/cvpr/WangWCLL0LH20.bib}, - booktitle = {2020 {IEEE/CVF} Conference on Computer Vision and Pattern Recognition, {CVPR} 2020, Seattle, WA, USA, June 13-19, 2020}, - doi = {10.1109/CVPR42600.2020.00215}, + booktitle = {2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, + doi = {10.1109/cvpr42600.2020.00215}, pages = {2075--2084}, - publisher = {{IEEE}}, + publisher = {IEEE}, timestamp = {Tue, 22 Dec 2020 00:00:00 +0100}, - title = {{APQ:} Joint Search for Network Architecture, Pruning and Quantization Policy}, - url = {https://doi.org/10.1109/CVPR42600.2020.00215}, - year = {2020} + title = {{APQ:} {Joint} Search for Network Architecture, Pruning and Quantization Policy}, + url = {https://doi.org/10.1109/cvpr42600.2020.00215}, + year = {2020}, + source = {Crossref}, + month = jun, } @book{weik1955survey, @@ -1112,7 +1191,7 @@ @book{weik1955survey language = {en}, publisher = {Ballistic Research Laboratories}, title = {A Survey of Domestic Electronic Digital Computing Systems}, - year = {1955} + year = {1955}, } @article{wong2012metal, @@ -1127,12 +1206,13 @@ @article{wong2012metal title = {{Metal{\textendash}Oxide} {RRAM}}, url = {https://doi.org/10.1109/jproc.2012.2190369}, volume = {100}, - year = {2012} + year = {2012}, + month = jun, } @article{xiong2021mribased, - abstract = {Brain tumor segmentation is a challenging problem in medical image processing and analysis. It is a very time-consuming and error-prone task. In order to reduce the burden on physicians and improve the segmentation accuracy, the computer-aided detection (CAD) systems need to be developed. Due to the powerful feature learning ability of the deep learning technology, many deep learning-based methods have been applied to the brain tumor segmentation CAD systems and achieved satisfactory accuracy. However, deep learning neural networks have high computational complexity, and the brain tumor segmentation process consumes significant time. Therefore, in order to achieve the high segmentation accuracy of brain tumors and obtain the segmentation results efficiently, it is very demanding to speed up the segmentation process of brain tumors.}, author = {Xiong, Siyu and Wu, Guoqing and Fan, Xitian and Feng, Xuan and Huang, Zhongcheng and Cao, Wei and Zhou, Xuegong and Ding, Shijin and Yu, Jinhua and Wang, Lingli and Shi, Zhifeng}, + abstract = {Brain tumor segmentation is a challenging problem in medical image processing and analysis. It is a very time-consuming and error-prone task. In order to reduce the burden on physicians and improve the segmentation accuracy, the computer-aided detection (CAD) systems need to be developed. Due to the powerful feature learning ability of the deep learning technology, many deep learning-based methods have been applied to the brain tumor segmentation CAD systems and achieved satisfactory accuracy. However, deep learning neural networks have high computational complexity, and the brain tumor segmentation process consumes significant time. Therefore, in order to achieve the high segmentation accuracy of brain tumors and obtain the segmentation results efficiently, it is very demanding to speed up the segmentation process of brain tumors.}, bdsk-url-1 = {https://doi.org/10.1186/s12859-021-04347-6}, doi = {10.1186/s12859-021-04347-6}, issn = {1471-2105}, @@ -1146,7 +1226,8 @@ @article{xiong2021mribased url = {https://doi.org/10.1186/s12859-021-04347-6}, urldate = {2023-11-07}, volume = {22}, - year = {2021} + year = {2021}, + month = sep, } @article{xiu2019time, @@ -1161,7 +1242,7 @@ @article{xiu2019time title = {Time Moore: {Exploiting} {Moore's} Law From The Perspective of Time}, url = {https://doi.org/10.1109/mssc.2018.2882285}, volume = {11}, - year = {2019} + year = {2019}, } @article{young2018recent, @@ -1176,23 +1257,28 @@ @article{young2018recent title = {Recent Trends in Deep Learning Based Natural Language Processing {[Review} Article]}, url = {https://doi.org/10.1109/mci.2018.2840738}, volume = {13}, - year = {2018} + year = {2018}, + month = aug, } @article{yu2023rl, + author = {Qian, Yu and Zhou, Xuegong and Zhou, Hao and Wang, Lingli}, abstract = {Logic synthesis is a crucial step in electronic design automation tools. The rapid developments of reinforcement learning (RL) have enabled the automated exploration of logic synthesis. Existing RL based methods may lead to data inefficiency, and the exploration approaches for FPGA and ASIC technology mapping in recent works lack the flexibility of the learning process. This work proposes ESE, a reinforcement learning based framework to efficiently learn the logic synthesis process. The framework supports the modeling of logic optimization and technology mapping for FPGA and ASIC. The optimization for the execution time of the synthesis script is also considered. For the modeling of FPGA mapping, the logic optimization and technology mapping are combined to be learned in a flexible way. For the modeling of ASIC mapping, the standard cell based optimization and LUT optimization operations are incorporated into the ASIC synthesis flow. To improve the utilization of samples, the Proximal Policy Optimization model is adopted. Furthermore, the framework is enhanced by supporting MIG based synthesis exploration. Experiments show that for FPGA technology mapping on the VTR benchmark, the average LUT-Level-Product and script runtime are improved by more than 18.3\% and 12.4\% respectively than previous works. For ASIC mapping on the EPFL benchmark, the average Area-Delay-Product is improved by 14.5\%.}, address = {New York, NY, USA}, - author = {Qian, Yu and Zhou, Xuegong and Zhou, Hao and Wang, Lingli}, doi = {10.1145/3632174}, - issn = {1084-4309}, + issn = {1084-4309, 1557-7309}, journal = {ACM Trans. Des. Autom. Electron. Syst.}, keywords = {technology mapping, Majority-Inverter Graph, And-Inverter Graph, Reinforcement learning, logic optimization}, - month = {nov}, + month = jan, note = {Just Accepted}, - publisher = {Association for Computing Machinery}, + publisher = {Association for Computing Machinery (ACM)}, title = {An Efficient Reinforcement Learning Based Framework for Exploring Logic Synthesis}, url = {https://doi.org/10.1145/3632174}, - year = {2023} + year = {2024}, + number = {2}, + source = {Crossref}, + volume = {29}, + pages = {1--33}, } @inproceedings{zhang2015fpga, @@ -1201,25 +1287,47 @@ @inproceedings{zhang2015fpga pages = {161--170}, title = {{FPGA}-based Accelerator Design for Deep Convolutional Neural Networks Proceedings of the 2015 {ACM}}, volume = {15}, - year = {2015} + year = {2015}, } @inproceedings{zhang2022fullstack, - abstract = {The rapidly-changing deep learning landscape presents a unique opportunity for building inference accelerators optimized for specific datacenter-scale workloads. We propose Full-stack Accelerator Search Technique (FAST), a hardware accelerator search framework that defines a broad optimization environment covering key design decisions within the hardware-software stack, including hardware datapath, software scheduling, and compiler passes such as operation fusion and tensor padding. In this paper, we analyze bottlenecks in state-of-the-art vision and natural language processing (NLP) models, including EfficientNet and BERT, and use FAST to design accelerators capable of addressing these bottlenecks. FAST-generated accelerators optimized for single workloads improve Perf/TDP by 3.7\texttimes{} on average across all benchmarks compared to TPU-v3. A FAST-generated accelerator optimized for serving a suite of workloads improves Perf/TDP by 2.4\texttimes{} on average compared to TPU-v3. Our return on investment analysis shows that FAST-generated accelerators can potentially be practical for moderate-sized datacenter deployments.}, - address = {New York, NY, USA}, author = {Zhang, Dan and Huda, Safeen and Songhori, Ebrahim and Prabhu, Kartik and Le, Quoc and Goldie, Anna and Mirhoseini, Azalia}, + abstract = {The rapidly-changing deep learning landscape presents a unique opportunity for building inference accelerators optimized for specific datacenter-scale workloads. We propose Full-stack Accelerator Search Technique (FAST), a hardware accelerator search framework that defines a broad optimization environment covering key design decisions within the hardware-software stack, including hardware datapath, software scheduling, and compiler passes such as operation fusion and tensor padding. In this paper, we analyze bottlenecks in state-of-the-art vision and natural language processing (NLP) models, including EfficientNet and BERT, and use FAST to design accelerators capable of addressing these bottlenecks. FAST-generated accelerators optimized for single workloads improve Perf/TDP by 3.7{\texttimes} on average across all benchmarks compared to TPU-v3. A FAST-generated accelerator optimized for serving a suite of workloads improves Perf/TDP by 2.4{\texttimes} on average compared to TPU-v3. Our return on investment analysis shows that FAST-generated accelerators can potentially be practical for moderate-sized datacenter deployments.}, + address = {New York, NY, USA}, booktitle = {Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems}, doi = {10.1145/3503222.3507767}, isbn = {9781450392051}, keywords = {design space exploration, hardware-software codesign, tensor processing unit, machine learning, operation fusion}, location = {Lausanne, Switzerland}, numpages = {16}, - pages = {27-42}, - publisher = {Association for Computing Machinery}, + pages = {27--42}, + publisher = {ACM}, series = {ASPLOS '22}, - title = {A Full-Stack Search Technique for Domain Optimized Deep Learning Accelerators}, + title = {A full-stack search technique for domain optimized deep learning accelerators}, url = {https://doi.org/10.1145/3503222.3507767}, - year = {2022} + year = {2022}, + source = {Crossref}, + month = feb, +} + +@inproceedings{zhangfast, + author = {Zhang, Dan and Huda, Safeen and Songhori, Ebrahim and Prabhu, Kartik and Le, Quoc and Goldie, Anna and Mirhoseini, Azalia}, + title = {A full-stack search technique for domain optimized deep learning accelerators}, + year = {2022}, + isbn = {9781450392051}, + publisher = {ACM}, + address = {New York, NY, USA}, + url = {https://doi.org/10.1145/3503222.3507767}, + doi = {10.1145/3503222.3507767}, + abstract = {The rapidly-changing deep learning landscape presents a unique opportunity for building inference accelerators optimized for specific datacenter-scale workloads. We propose Full-stack Accelerator Search Technique (FAST), a hardware accelerator search framework that defines a broad optimization environment covering key design decisions within the hardware-software stack, including hardware datapath, software scheduling, and compiler passes such as operation fusion and tensor padding. In this paper, we analyze bottlenecks in state-of-the-art vision and natural language processing (NLP) models, including EfficientNet and BERT, and use FAST to design accelerators capable of addressing these bottlenecks. FAST-generated accelerators optimized for single workloads improve Perf/TDP by 3.7{\texttimes} on average across all benchmarks compared to TPU-v3. A FAST-generated accelerator optimized for serving a suite of workloads improves Perf/TDP by 2.4{\texttimes} on average compared to TPU-v3. Our return on investment analysis shows that FAST-generated accelerators can potentially be practical for moderate-sized datacenter deployments.}, + booktitle = {Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems}, + pages = {27--42}, + numpages = {16}, + keywords = {design space exploration, hardware-software codesign, tensor processing unit, machine learning, operation fusion}, + location = {Lausanne, Switzerland}, + series = {ASPLOS '22}, + source = {Crossref}, + month = feb, } @article{zhou2022photonic, @@ -1234,15 +1342,21 @@ @article{zhou2022photonic title = {Photonic matrix multiplication lights up photonic accelerator and beyond}, url = {https://doi.org/10.1038/s41377-022-00717-8}, volume = {11}, - year = {2022} + year = {2022}, + month = feb, } @inproceedings{zhou2023area, - author = {Zhou, Guanglei and Anderson, Jason H}, + author = {Zhou, Guanglei and Anderson, Jason H.}, booktitle = {Proceedings of the 28th Asia and South Pacific Design Automation Conference}, pages = {159--165}, - title = {Area-Driven FPGA Logic Synthesis Using Reinforcement Learning}, - year = {2023} + title = {Area-Driven {FPGA} Logic Synthesis Using Reinforcement Learning}, + year = {2023}, + doi = {10.1145/3566097.3567894}, + source = {Crossref}, + url = {https://doi.org/10.1145/3566097.3567894}, + publisher = {ACM}, + month = jan, } @inproceedings{zhu2018benchmarking, @@ -1255,34 +1369,6 @@ @inproceedings{zhu2018benchmarking source = {Crossref}, title = {Benchmarking and Analyzing Deep Neural Network Training}, url = {https://doi.org/10.1109/iiswc.2018.8573476}, - year = {2018} -} - -@inproceedings{zhangfast, -author = {Zhang, Dan and Huda, Safeen and Songhori, Ebrahim and Prabhu, Kartik and Le, Quoc and Goldie, Anna and Mirhoseini, Azalia}, -title = {A Full-Stack Search Technique for Domain Optimized Deep Learning Accelerators}, -year = {2022}, -isbn = {9781450392051}, -publisher = {Association for Computing Machinery}, -address = {New York, NY, USA}, -url = {https://doi.org/10.1145/3503222.3507767}, -doi = {10.1145/3503222.3507767}, -abstract = {The rapidly-changing deep learning landscape presents a unique opportunity for building inference accelerators optimized for specific datacenter-scale workloads. We propose Full-stack Accelerator Search Technique (FAST), a hardware accelerator search framework that defines a broad optimization environment covering key design decisions within the hardware-software stack, including hardware datapath, software scheduling, and compiler passes such as operation fusion and tensor padding. In this paper, we analyze bottlenecks in state-of-the-art vision and natural language processing (NLP) models, including EfficientNet and BERT, and use FAST to design accelerators capable of addressing these bottlenecks. FAST-generated accelerators optimized for single workloads improve Perf/TDP by 3.7\texttimes{} on average across all benchmarks compared to TPU-v3. A FAST-generated accelerator optimized for serving a suite of workloads improves Perf/TDP by 2.4\texttimes{} on average compared to TPU-v3. Our return on investment analysis shows that FAST-generated accelerators can potentially be practical for moderate-sized datacenter deployments.}, -booktitle = {Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems}, -pages = {27-42}, -numpages = {16}, -keywords = {design space exploration, hardware-software codesign, tensor processing unit, machine learning, operation fusion}, -location = {Lausanne, Switzerland}, -series = {ASPLOS '22} + year = {2018}, + month = sep, } - -@article{huang2022flexible, - title={How Flexible is Your Computing System?}, - author={Huang, Shihua and Waeijen, Luc and Corporaal, Henk}, - journal={ACM Transactions on Embedded Computing Systems (TECS)}, - volume={21}, - number={4}, - pages={1--41}, - year={2022}, - publisher={ACM New York, NY} -} \ No newline at end of file diff --git a/contents/image_classification/image_classification.bib b/contents/image_classification/image_classification.bib index e69de29b..00614696 100644 --- a/contents/image_classification/image_classification.bib +++ b/contents/image_classification/image_classification.bib @@ -0,0 +1,2 @@ +%comment{This file was created with betterbib v5.0.11.} + diff --git a/contents/introduction/introduction.bib b/contents/introduction/introduction.bib index b7a052cc..d4ae204a 100644 --- a/contents/introduction/introduction.bib +++ b/contents/introduction/introduction.bib @@ -1,10 +1,18 @@ +%comment{This file was created with betterbib v5.0.11.} + + @article{weiser1991computer, - title={The Computer for the 21 st Century}, - author={Weiser, Mark}, - journal={Scientific american}, - volume={265}, - number={3}, - pages={94--105}, - year={1991}, - publisher={JSTOR} + author = {Weiser, Mark}, + title = {The Computer for the 21st Century}, + journal = {Sci. Am.}, + volume = {265}, + number = {3}, + pages = {94--104}, + year = {1991}, + publisher = {Springer Science and Business Media LLC}, + doi = {10.1038/scientificamerican0991-94}, + source = {Crossref}, + url = {https://doi.org/10.1038/scientificamerican0991-94}, + issn = {0036-8733}, + month = sep, } diff --git a/contents/kws_feature_eng/kws_feature_eng.bib b/contents/kws_feature_eng/kws_feature_eng.bib index e69de29b..00614696 100644 --- a/contents/kws_feature_eng/kws_feature_eng.bib +++ b/contents/kws_feature_eng/kws_feature_eng.bib @@ -0,0 +1,2 @@ +%comment{This file was created with betterbib v5.0.11.} + diff --git a/contents/kws_nicla/kws_nicla.bib b/contents/kws_nicla/kws_nicla.bib index e69de29b..00614696 100644 --- a/contents/kws_nicla/kws_nicla.bib +++ b/contents/kws_nicla/kws_nicla.bib @@ -0,0 +1,2 @@ +%comment{This file was created with betterbib v5.0.11.} + diff --git a/contents/ml_systems/ml_systems.bib b/contents/ml_systems/ml_systems.bib index 66e945e3..bea5df32 100644 --- a/contents/ml_systems/ml_systems.bib +++ b/contents/ml_systems/ml_systems.bib @@ -1,6 +1,9 @@ +%comment{This file was created with betterbib v5.0.11.} + + @misc{armcomfuture, author = {ARM.com}, howpublished = {https://www.arm.com/company/news/2023/02/arm-announces-q3-fy22-results}, note = {(Accessed on 09/16/2023)}, - title = {The future is being built on Arm: {Market} diversification continues to drive strong royalty and licensing growth as ecosystem reaches quarter of a trillion chips milestone {\textendash} Arm{\textregistered}} + title = {The future is being built on Arm: {Market} diversification continues to drive strong royalty and licensing growth as ecosystem reaches quarter of a trillion chips milestone {\textendash} Arm{\textregistered}}, } diff --git a/contents/motion_classify_ad/motion_classify_ad.bib b/contents/motion_classify_ad/motion_classify_ad.bib index e69de29b..00614696 100644 --- a/contents/motion_classify_ad/motion_classify_ad.bib +++ b/contents/motion_classify_ad/motion_classify_ad.bib @@ -0,0 +1,2 @@ +%comment{This file was created with betterbib v5.0.11.} + diff --git a/contents/niclav_sys/niclav_sys.bib b/contents/niclav_sys/niclav_sys.bib index e69de29b..00614696 100644 --- a/contents/niclav_sys/niclav_sys.bib +++ b/contents/niclav_sys/niclav_sys.bib @@ -0,0 +1,2 @@ +%comment{This file was created with betterbib v5.0.11.} + diff --git a/contents/object_detection_fomo/object_detection_fomo.bib b/contents/object_detection_fomo/object_detection_fomo.bib index e69de29b..00614696 100644 --- a/contents/object_detection_fomo/object_detection_fomo.bib +++ b/contents/object_detection_fomo/object_detection_fomo.bib @@ -0,0 +1,2 @@ +%comment{This file was created with betterbib v5.0.11.} + diff --git a/contents/ondevice_learning/ondevice_learning.bib b/contents/ondevice_learning/ondevice_learning.bib index 1316b2d8..afc583f7 100644 --- a/contents/ondevice_learning/ondevice_learning.bib +++ b/contents/ondevice_learning/ondevice_learning.bib @@ -1,6 +1,9 @@ +%comment{This file was created with betterbib v5.0.11.} + + @inproceedings{abadi2016deep, - address = {New York, NY, USA}, author = {Abadi, Martin and Chu, Andy and Goodfellow, Ian and McMahan, H. Brendan and Mironov, Ilya and Talwar, Kunal and Zhang, Li}, + address = {New York, NY, USA}, booktitle = {Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security}, date-added = {2023-11-22 18:06:03 -0500}, date-modified = {2023-11-22 18:08:42 -0500}, @@ -12,19 +15,20 @@ @inproceedings{abadi2016deep source = {Crossref}, title = {Deep Learning with Differential Privacy}, url = {https://doi.org/10.1145/2976749.2978318}, - year = {2016} + year = {2016}, + month = oct, } @inproceedings{cai2020tinytl, - author = {Han Cai and Chuang Gan and Ligeng Zhu and Song Han}, + author = {Cai, Han and Gan, Chuang and Zhu, Ligeng and Han, Song}, + editor = {Larochelle, Hugo and Ranzato, Marc'Aurelio and Hadsell, Raia and Balcan, Maria-Florina and Lin, Hsuan-Tien}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/CaiGZ020.bib}, booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual}, - editor = {Hugo Larochelle and Marc'Aurelio Ranzato and Raia Hadsell and Maria{-}Florina Balcan and Hsuan{-}Tien Lin}, timestamp = {Tue, 19 Jan 2021 00:00:00 +0100}, - title = {TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning}, + title = {{TinyTL:} {Reduce} Memory, Not Parameters for Efficient On-Device Learning}, url = {https://proceedings.neurips.cc/paper/2020/hash/81f7acabd411274fcf65ce2070ed568a-Abstract.html}, - year = {2020} + year = {2020}, } @article{chen2016training, @@ -33,7 +37,7 @@ @article{chen2016training title = {Training deep nets with sublinear memory cost}, url = {https://arxiv.org/abs/1604.06174}, volume = {abs/1604.06174}, - year = {2016} + year = {2016}, } @inproceedings{chen2018tvm, @@ -41,7 +45,7 @@ @inproceedings{chen2018tvm booktitle = {13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)}, pages = {578--594}, title = {{TVM:} {An} automated End-to-End optimizing compiler for deep learning}, - year = {2018} + year = {2018}, } @article{chen2023learning, @@ -56,7 +60,8 @@ @article{chen2023learning title = {Learning domain-heterogeneous speaker recognition systems with personalized continual federated learning}, url = {https://doi.org/10.1186/s13636-023-00299-2}, volume = {2023}, - year = {2023} + year = {2023}, + month = sep, } @article{david2021tensorflow, @@ -65,7 +70,7 @@ @article{david2021tensorflow pages = {800--811}, title = {Tensorflow lite micro: {Embedded} machine learning for tinyml systems}, volume = {3}, - year = {2021} + year = {2021}, } @article{desai2016five, @@ -74,7 +79,7 @@ @article{desai2016five pages = {28}, title = {Five Safes: {Designing} data access for research}, volume = {1601}, - year = {2016} + year = {2016}, } @article{dhar2021survey, @@ -90,7 +95,8 @@ @article{dhar2021survey title = {A Survey of On-Device Machine Learning}, url = {https://doi.org/10.1145/3450494}, volume = {2}, - year = {2021} + year = {2021}, + month = jul, } @article{dwork2014algorithmic, @@ -105,7 +111,7 @@ @article{dwork2014algorithmic title = {The Algorithmic Foundations of Differential Privacy}, url = {https://doi.org/10.1561/0400000042}, volume = {9}, - year = {2013} + year = {2013}, } @article{esteva2017dermatologist, @@ -120,20 +126,21 @@ @article{esteva2017dermatologist title = {Dermatologist-level classification of skin cancer with deep neural networks}, url = {https://doi.org/10.1038/nature21056}, volume = {542}, - year = {2017} + year = {2017}, + month = jan, } @inproceedings{gruslys2016memory, - author = {Audrunas Gruslys and R{\'{e}}mi Munos and Ivo Danihelka and Marc Lanctot and Alex Graves}, + author = {Gruslys, Audrunas and Munos, R\'emi and Danihelka, Ivo and Lanctot, Marc and Graves, Alex}, + editor = {Lee, Daniel D. and Sugiyama, Masashi and von Luxburg, Ulrike and Guyon, Isabelle and Garnett, Roman}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/GruslysMDLG16.bib}, booktitle = {Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain}, - editor = {Daniel D. Lee and Masashi Sugiyama and Ulrike von Luxburg and Isabelle Guyon and Roman Garnett}, pages = {4125--4133}, timestamp = {Thu, 21 Jan 2021 00:00:00 +0100}, title = {Memory-Efficient Backpropagation Through Time}, url = {https://proceedings.neurips.cc/paper/2016/hash/a501bebf79d570651ff601788ea9d16d-Abstract.html}, - year = {2016} + year = {2016}, } @inproceedings{hong2023publishing, @@ -146,20 +153,21 @@ @inproceedings{hong2023publishing source = {Crossref}, title = {Publishing Efficient On-device Models Increases Adversarial Vulnerability}, url = {https://doi.org/10.1109/satml54575.2023.00026}, - year = {2023} + year = {2023}, + month = feb, } @inproceedings{kairouz2015secure, - author = {Peter Kairouz and Sewoong Oh and Pramod Viswanath}, + author = {Kairouz, Peter and Oh, Sewoong and Viswanath, Pramod}, + editor = {Cortes, Corinna and Lawrence, Neil D. and Lee, Daniel D. and Sugiyama, Masashi and Garnett, Roman}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/KairouzOV15.bib}, booktitle = {Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada}, - editor = {Corinna Cortes and Neil D. Lawrence and Daniel D. Lee and Masashi Sugiyama and Roman Garnett}, pages = {2008--2016}, timestamp = {Thu, 21 Jan 2021 00:00:00 +0100}, title = {Secure Multi-party Differential Privacy}, url = {https://proceedings.neurips.cc/paper/2015/hash/a01610228fe998f515a72dd730294d87-Abstract.html}, - year = {2015} + year = {2015}, } @article{karargyris2023federated, @@ -174,7 +182,8 @@ @article{karargyris2023federated title = {Federated benchmarking of medical artificial intelligence with {MedPerf}}, url = {https://doi.org/10.1038/s42256-023-00652-2}, volume = {5}, - year = {2023} + year = {2023}, + month = jul, } @article{kwon2023tinytrain, @@ -183,32 +192,32 @@ @article{kwon2023tinytrain title = {{TinyTrain:} {Deep} Neural Network Training at the Extreme Edge}, url = {https://arxiv.org/abs/2307.09988}, volume = {abs/2307.09988}, - year = {2023} + year = {2023}, } @inproceedings{li2016lightrnn, - author = {Xiang Li and Tao Qin and Jian Yang and Tie{-}Yan Liu}, + author = {Li, Xiang and Qin, Tao and Yang, Jian and Liu, Tie-Yan}, + editor = {Lee, Daniel D. and Sugiyama, Masashi and von Luxburg, Ulrike and Guyon, Isabelle and Garnett, Roman}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/LiQYHL16.bib}, booktitle = {Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain}, - editor = {Daniel D. Lee and Masashi Sugiyama and Ulrike von Luxburg and Isabelle Guyon and Roman Garnett}, pages = {4385--4393}, timestamp = {Thu, 21 Jan 2021 00:00:00 +0100}, - title = {LightRNN: Memory and Computation-Efficient Recurrent Neural Networks}, + title = {{LightRNN:} {Memory} and Computation-Efficient Recurrent Neural Networks}, url = {https://proceedings.neurips.cc/paper/2016/hash/c3e4035af2a1cde9f21e1ae1951ac80b-Abstract.html}, - year = {2016} + year = {2016}, } @inproceedings{lin2020mcunet, - author = {Ji Lin and Wei{-}Ming Chen and Yujun Lin and John Cohn and Chuang Gan and Song Han}, + author = {Lin, Ji and Chen, Wei-Ming and Lin, Yujun and Cohn, John and Gan, Chuang and Han, Song}, + editor = {Larochelle, Hugo and Ranzato, Marc'Aurelio and Hadsell, Raia and Balcan, Maria-Florina and Lin, Hsuan-Tien}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/LinCLCG020.bib}, booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual}, - editor = {Hugo Larochelle and Marc'Aurelio Ranzato and Raia Hadsell and Maria{-}Florina Balcan and Hsuan{-}Tien Lin}, timestamp = {Thu, 11 Feb 2021 00:00:00 +0100}, - title = {MCUNet: Tiny Deep Learning on IoT Devices}, + title = {{MCUNet:} {Tiny} Deep Learning on {IoT} Devices}, url = {https://proceedings.neurips.cc/paper/2020/hash/86c51678350f656dcc7f490a43946ee5-Abstract.html}, - year = {2020} + year = {2020}, } @article{lin2022device, @@ -217,7 +226,7 @@ @article{lin2022device pages = {22941--22954}, title = {On-device training under 256kb memory}, volume = {35}, - year = {2022} + year = {2022}, } @article{moshawrab2023reviewing, @@ -232,7 +241,8 @@ @article{moshawrab2023reviewing title = {Reviewing Federated Learning Aggregation Algorithms; Strategies, Contributions, Limitations and Future Perspectives}, url = {https://doi.org/10.3390/electronics12102287}, volume = {12}, - year = {2023} + year = {2023}, + month = may, } @inproceedings{nguyen2023re, @@ -244,7 +254,8 @@ @inproceedings{nguyen2023re source = {Crossref}, title = {Re-Thinking Model Inversion Attacks Against Deep Neural Networks}, url = {https://doi.org/10.1109/cvpr52729.2023.01572}, - year = {2023} + year = {2023}, + month = jun, } @article{pan2009survey, @@ -259,7 +270,8 @@ @article{pan2009survey title = {A Survey on Transfer Learning}, url = {https://doi.org/10.1109/tkde.2009.191}, volume = {22}, - year = {2010} + year = {2010}, + month = oct, } @inproceedings{rouhani2017tinydl, @@ -272,7 +284,8 @@ @inproceedings{rouhani2017tinydl source = {Crossref}, title = {{TinyDL:} {Just-in-time} deep learning solution for constrained embedded systems}, url = {https://doi.org/10.1109/iscas.2017.8050343}, - year = {2017} + year = {2017}, + month = may, } @inproceedings{shi2022data, @@ -284,7 +297,8 @@ @inproceedings{shi2022data source = {Crossref}, title = {Data selection for efficient model update in federated learning}, url = {https://doi.org/10.1145/3517207.3526980}, - year = {2022} + year = {2022}, + month = apr, } @article{wu2022sustainable, @@ -293,7 +307,7 @@ @article{wu2022sustainable pages = {795--813}, title = {Sustainable ai: {Environmental} implications, challenges and opportunities}, volume = {4}, - year = {2022} + year = {2022}, } @article{xu2023federated, @@ -302,7 +316,7 @@ @article{xu2023federated title = {Federated Learning of Gboard Language Models with Differential Privacy}, url = {https://arxiv.org/abs/2305.18465}, volume = {abs/2305.18465}, - year = {2023} + year = {2023}, } @inproceedings{yang2023online, @@ -315,7 +329,8 @@ @inproceedings{yang2023online source = {Crossref}, title = {Online Model Compression for Federated Learning with Large Models}, url = {https://doi.org/10.1109/icassp49357.2023.10097124}, - year = {2023} + year = {2023}, + month = jun, } @article{zhao2018federated, @@ -324,25 +339,22 @@ @article{zhao2018federated title = {Federated learning with non-iid data}, url = {https://arxiv.org/abs/1806.00582}, volume = {abs/1806.00582}, - year = {2018} + year = {2018}, } @article{zhuang2021comprehensive, - author={Zhuang, Fuzhen and Qi, Zhiyuan and Duan, Keyu and Xi, Dongbo and Zhu, Yongchun and Zhu, Hengshu and Xiong, Hui and He, Qing}, - journal={Proceedings of the IEEE}, - title={A Comprehensive Survey on Transfer Learning}, - year={2021}, - volume={109}, - number={1}, - pages={43-76}, - keywords={Transfer learning;Semisupervised learning;Data models;Covariance matrices;Machine learning;Adaptation models;Domain adaptation;interpretation;machine learning;transfer learning}, - doi={10.1109/JPROC.2020.3004555} + author = {Zhuang, Fuzhen and Qi, Zhiyuan and Duan, Keyu and Xi, Dongbo and Zhu, Yongchun and Zhu, Hengshu and Xiong, Hui and He, Qing}, + journal = {Proc. IEEE}, + title = {A Comprehensive Survey on Transfer Learning}, + year = {2021}, + volume = {109}, + number = {1}, + pages = {43--76}, + keywords = {Transfer learning;Semisupervised learning;Data models;Covariance matrices;Machine learning;Adaptation models;Domain adaptation;interpretation;machine learning;transfer learning}, + doi = {10.1109/jproc.2020.3004555}, + source = {Crossref}, + url = {https://doi.org/10.1109/jproc.2020.3004555}, + publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, + issn = {0018-9219, 1558-2256}, + month = jan, } - -@inproceedings{cai2020tinytl, - title = {TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning}, - author = {Cai, Han and Gan, Chuang and Zhu, Ligeng and Han, Song}, - booktitle = {Advances in Neural Information Processing Systems}, - volume = {33}, - year = {2020} -} \ No newline at end of file diff --git a/contents/ops/ops.bib b/contents/ops/ops.bib index 946765e8..34f3b122 100644 --- a/contents/ops/ops.bib +++ b/contents/ops/ops.bib @@ -1,3 +1,6 @@ +%comment{This file was created with betterbib v5.0.11.} + + @article{attia2018noninvasive, author = {Attia, Zachi I. and Sugrue, Alan and Asirvatham, Samuel J. and Ackerman, Michael J. and Kapa, Suraj and Friedman, Paul A. and Noseworthy, Peter A.}, bdsk-url-1 = {https://doi.org/10.1371/journal.pone.0201059}, @@ -11,7 +14,8 @@ @article{attia2018noninvasive title = {Noninvasive assessment of dofetilide plasma concentration using a deep learning (neural network) analysis of the surface electrocardiogram: {A} proof of concept study}, url = {https://doi.org/10.1371/journal.pone.0201059}, volume = {13}, - year = {2018} + year = {2018}, + month = aug, } @article{chen2023framework, @@ -25,7 +29,8 @@ @article{chen2023framework source = {Crossref}, title = {A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring}, url = {https://doi.org/10.1038/s41551-023-01115-0}, - year = {2023} + year = {2023}, + month = nov, } @article{guo2019mobile, @@ -41,7 +46,8 @@ @article{guo2019mobile title = {Mobile Photoplethysmographic Technology to Detect Atrial Fibrillation}, url = {https://doi.org/10.1016/j.jacc.2019.08.019}, volume = {74}, - year = {2019} + year = {2019}, + month = nov, } @article{janapa2023edge, @@ -49,7 +55,7 @@ @article{janapa2023edge journal = {Proceedings of Machine Learning and Systems}, title = {Edge Impulse: {An} {MLOps} Platform for Tiny Machine Learning}, volume = {5}, - year = {2023} + year = {2023}, } @article{li2021noninvasive, @@ -65,7 +71,8 @@ @article{li2021noninvasive title = {Non-invasive Monitoring of Three Glucose Ranges Based On {ECG} By Using {DBSCAN}-{CNN}}, url = {https://doi.org/10.1109/jbhi.2021.3072628}, volume = {25}, - year = {2021} + year = {2021}, + month = sep, } @article{liu2022monitoring, @@ -83,12 +90,13 @@ @article{liu2022monitoring title = {Monitoring gait at home with radio waves in Parkinson{\textquoteright}s disease: {A} marker of severity, progression, and medication response}, url = {https://doi.org/10.1126/scitranslmed.adc9669}, volume = {14}, - year = {2022} + year = {2022}, + month = sep, } @article{psoma2023wearable, - article-number = {719}, author = {Psoma, Sotiria D. and Kanthou, Chryso}, + article-number = {719}, bdsk-url-1 = {https://www.mdpi.com/2079-6374/13/7/719}, bdsk-url-2 = {https://doi.org/10.3390/bios13070719}, doi = {10.3390/bios13070719}, @@ -102,8 +110,27 @@ @article{psoma2023wearable title = {Wearable Insulin Biosensors for Diabetes Management: {Advances} and Challenges}, url = {https://doi.org/10.3390/bios13070719}, volume = {13}, - year = {2023} -}} + year = {2023}, + month = jul, +} + +@inproceedings{sambasivan2021, + author = {Sambasivan, Nithya and Kapania, Shivani and Highfill, Hannah and Akrong, Diana and Paritosh, Praveen and Aroyo, Lora M}, + title = {{{\textquotedblleft}Everyone} wants to do the model work, not the data work{\textquotedblright}: {Data} Cascades in High-Stakes {AI}}, + year = {2021}, + isbn = {9781450380966}, + publisher = {ACM}, + address = {New York, NY, USA}, + url = {https://doi.org/10.1145/3411764.3445518}, + doi = {10.1145/3411764.3445518}, + booktitle = {Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems}, + articleno = {39}, + numpages = {15}, + location = {conf-loc, cityYokohama/city, countryJapan/country, /conf-loc}, + series = {CHI '21}, + source = {Crossref}, + month = may, +} @inproceedings{sculley2015hidden, author = {Sambasivan, Nithya and Kapania, Shivani and Highfill, Hannah and Akrong, Diana and Paritosh, Praveen and Aroyo, Lora M}, @@ -113,7 +140,17 @@ @inproceedings{sculley2015hidden source = {Crossref}, title = {{{\textquotedblleft}Everyone} wants to do the model work, not the data work{\textquotedblright}: {Data} Cascades in High-Stakes {AI}}, url = {https://doi.org/10.1145/3411764.3445518}, - year = {2021} + year = {2021}, + month = may, +} + +@manual{stm2021l4, + organization = {STMicroelectronics}, + title = {{Stm32L4Q5Ag}}, + number = {DS12902}, + year = {2021}, + month = nov, + note = {Rev. 3}, } @article{zhang2017highly, @@ -130,30 +167,6 @@ @article{zhang2017highly title = {Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals}, url = {https://doi.org/10.1186/s12938-017-0317-z}, volume = {16}, - year = {2017} -} - -@inproceedings{sambasivan2021, - author = {Sambasivan, Nithya and Kapania, Shivani and Highfill, Hannah and Akrong, Diana and Paritosh, Praveen and Aroyo, Lora M}, - title = {Everyone wants to do the model work, not the data work: Data Cascades in High-Stakes AI}, - year = {2021}, - isbn = {9781450380966}, - publisher = {Association for Computing Machinery}, - address = {New York, NY, USA}, - url = {https://doi.org/10.1145/3411764.3445518}, - doi = {10.1145/3411764.3445518}, - booktitle = {Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems}, - articleno = {39}, - numpages = {15}, - location = {, Yokohama, Japan, }, - series = {CHI '21} + year = {2017}, + month = feb, } - -@manual{stm2021l4, - organization = "STMicroelectronics", - title = "STM32L4Q5AG", - number = "DS12902", - year = 2021, - month = 11, - note = "Rev. 3" -} \ No newline at end of file diff --git a/contents/optimizations/optimizations.bib b/contents/optimizations/optimizations.bib index 2964e4d4..ca44d62b 100644 --- a/contents/optimizations/optimizations.bib +++ b/contents/optimizations/optimizations.bib @@ -1,65 +1,73 @@ +%comment{This file was created with betterbib v5.0.11.} + + @inproceedings{benmeziane2021hardwareaware, author = {Benmeziane, Hadjer and El Maghraoui, Kaoutar and Ouarnoughi, Hamza and Niar, Smail and Wistuba, Martin and Wang, Naigang}, + editor = {Zhou, Zhi-Hua}, bdsk-url-1 = {https://doi.org/10.24963/ijcai.2021/592}, booktitle = {Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence}, doi = {10.24963/ijcai.2021/592}, - editor = {Zhou, Zhi-Hua}, note = {Survey Track}, pages = {4322--4329}, publisher = {International Joint Conferences on Artificial Intelligence Organization}, source = {Crossref}, title = {Hardware-Aware Neural Architecture Search: {Survey} and Taxonomy}, url = {https://doi.org/10.24963/ijcai.2021/592}, - year = {2021} + year = {2021}, + month = aug, } @inproceedings{cai2018proxylessnas, - author = {Han Cai and Ligeng Zhu and Song Han}, + author = {Cai, Han and Zhu, Ligeng and Han, Song}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/iclr/CaiZH19.bib}, - booktitle = {7th International Conference on Learning Representations, {ICLR} 2019, New Orleans, LA, USA, May 6-9, 2019}, + booktitle = {7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019}, publisher = {OpenReview.net}, timestamp = {Tue, 24 Nov 2020 00:00:00 +0100}, - title = {ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware}, + title = {{ProxylessNAS:} {Direct} Neural Architecture Search on Target Task and Hardware}, url = {https://openreview.net/forum?id=HylVB3AqYm}, - year = {2019} + year = {2019}, } @inproceedings{chu2021discovering, - author = {Grace Chu and Okan Arikan and Gabriel Bender and Weijun Wang and Achille Brighton and Pieter{-}Jan Kindermans and Hanxiao Liu and Berkin Akin and Suyog Gupta and Andrew Howard}, + author = {Chu, Grace and Arikan, Okan and Bender, Gabriel and Wang, Weijun and Brighton, Achille and Kindermans, Pieter-Jan and Liu, Hanxiao and Akin, Berkin and Gupta, Suyog and Howard, Andrew}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/cvpr/ChuABWBKLAG021.bib}, - booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition Workshops, {CVPR} Workshops 2021, virtual, June 19-25, 2021}, - doi = {10.1109/CVPRW53098.2021.00337}, + booktitle = {2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, + doi = {10.1109/cvprw53098.2021.00337}, pages = {3022--3031}, - publisher = {Computer Vision Foundation / {IEEE}}, + publisher = {IEEE}, timestamp = {Mon, 18 Jul 2022 01:00:00 +0200}, title = {Discovering Multi-Hardware Mobile Models via Architecture Search}, - url = {https://openaccess.thecvf.com/content/CVPR2021W/ECV/html/Chu\_Discovering\_Multi-Hardware\_Mobile\_Models\_via\_Architecture\_Search\_CVPRW\_2021\_paper.html}, - year = {2021} + url = {https://doi.org/10.1109/cvprw53098.2021.00337}, + year = {2021}, + source = {Crossref}, + month = jun, } @inproceedings{dong2022splitnets, - author = {Xin Dong and Barbara De Salvo and Meng Li and Chiao Liu and Zhongnan Qu and H. T. Kung and Ziyun Li}, + author = {Dong, Xin and De Salvo, Barbara and Li, Meng and Liu, Chiao and Qu, Zhongnan and Kung, H.T. and Li, Ziyun}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/cvpr/DongSLLQ0L22.bib}, - booktitle = {{IEEE/CVF} Conference on Computer Vision and Pattern Recognition, {CVPR} 2022, New Orleans, LA, USA, June 18-24, 2022}, - doi = {10.1109/CVPR52688.2022.01223}, + booktitle = {2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, + doi = {10.1109/cvpr52688.2022.01223}, pages = {12549--12559}, - publisher = {{IEEE}}, + publisher = {IEEE}, timestamp = {Sun, 22 Jan 2023 00:00:00 +0100}, - title = {SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems}, - url = {https://doi.org/10.1109/CVPR52688.2022.01223}, - year = {2022} + title = {{SplitNets:} {Designing} Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems}, + url = {https://doi.org/10.1109/cvpr52688.2022.01223}, + year = {2022}, + source = {Crossref}, + month = jun, } @misc{fahim2021hls4ml, - archiveprefix = {arXiv}, author = {Fahim, Farah and Hawks, Benjamin and Herwig, Christian and Hirschauer, James and Jindariani, Sergo and Tran, Nhan and Carloni, Luca P. and Guglielmo, Giuseppe Di and Harris, Philip and Krupa, Jeffrey and Rankin, Dylan and Valentin, Manuel Blanco and Hester, Josiah and Luo, Yingyi and Mamish, John and Orgrenci-Memik, Seda and Aarrestad, Thea and Javed, Hamza and Loncar, Vladimir and Pierini, Maurizio and Pol, Adrian Alan and Summers, Sioni and Duarte, Javier and Hauck, Scott and Hsu, Shih-Chieh and Ngadiuba, Jennifer and Liu, Mia and Hoang, Duc and Kreinar, Edward and Wu, Zhenbin}, + archiveprefix = {arXiv}, eprint = {2103.05579}, primaryclass = {cs.LG}, title = {hls4ml: {An} Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices}, - year = {2021} + year = {2021}, } @misc{gholami2021survey, @@ -68,7 +76,7 @@ @misc{gholami2021survey title = {A Survey of Quantization Methods for Efficient Neural Network Inference)}, url = {https://arxiv.org/abs/2103.13630}, volume = {abs/2103.13630}, - year = {2021} + year = {2021}, } @misc{google2023three, @@ -77,7 +85,7 @@ @misc{google2023three title = {Three Floating Point Formats}, url = {https://storage.googleapis.com/gweb-cloudblog-publish/images/Three\_floating-point\_formats.max-624x261.png}, urldate = {2023-10-20}, - year = {2023} + year = {2023}, } @inproceedings{gordon2018morphnet, @@ -89,7 +97,8 @@ @inproceedings{gordon2018morphnet source = {Crossref}, title = {{MorphNet:} {Fast} \& Simple Resource-Constrained Structure Learning of Deep Networks}, url = {https://doi.org/10.1109/cvpr.2018.00171}, - year = {2018} + year = {2018}, + month = jun, } @misc{gu2023deep, @@ -98,16 +107,28 @@ @misc{gu2023deep title = {Deep Learning Model Compression (ii) by Ivy Gu Medium}, url = {https://ivygdy.medium.com/deep-learning-model-compression-ii-546352ea9453}, urldate = {2023-10-20}, - year = {2023} + year = {2023}, } -@misc{han2015deep, - author = {Han and Mao and Dally}, - journal = {ArXiv preprint}, - title = {Deep Compression: {Compressing} Deep Neural Networks with Pruning, Trained Quantization and {Huffman} Coding}, - url = {https://arxiv.org/abs/1510.00149}, - volume = {abs/1510.00149}, - year = {2015} +@article{han2015deep, + title={Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding}, + author={Han, Song and Mao, Huizi and Dally, William J}, + journal={arXiv preprint arXiv:1510.00149}, + year={2015} +} + +@article{hawks2021psandqs, + author = {Hawks, Benjamin and Duarte, Javier and Fraser, Nicholas J. and Pappalardo, Alessandro and Tran, Nhan and Umuroglu, Yaman}, + title = {Ps and Qs: {Quantization-aware} Pruning for Efficient Low Latency Neural Network Inference}, + volume = {4}, + issn = {2624-8212}, + url = {https://doi.org/10.3389/frai.2021.676564}, + doi = {10.3389/frai.2021.676564}, + journal = {Frontiers in Artificial Intelligence}, + publisher = {Frontiers Media SA}, + year = {2021}, + month = jul, + source = {Crossref}, } @misc{hegde2023introduction, @@ -116,12 +137,12 @@ @misc{hegde2023introduction title = {An Introduction to Separable Convolutions - Analytics Vidhya}, url = {https://www.analyticsvidhya.com/blog/2021/11/an-introduction-to-separable-convolutions/}, urldate = {2023-10-20}, - year = {2023} + year = {2023}, } @misc{hinton2015distilling, - archiveprefix = {arXiv}, author = {Hinton, Geoffrey}, + archiveprefix = {arXiv}, doi = {10.1002/0471743984.vse0673}, eprint = {1503.02531}, isbn = {9780471332305, 9780471743989}, @@ -130,7 +151,8 @@ @misc{hinton2015distilling source = {Crossref}, title = {Van {Nostrand's} Scientific Encyclopedia}, url = {https://doi.org/10.1002/0471743984.vse0673}, - year = {2005} + year = {2005}, + month = oct, } @misc{howard2017mobilenets, @@ -139,7 +161,7 @@ @misc{howard2017mobilenets title = {{MobileNets:} {Efficient} Convolutional Neural Networks for Mobile Vision Applications}, url = {https://arxiv.org/abs/1704.04861}, volume = {abs/1704.04861}, - year = {2017} + year = {2017}, } @article{iandola2016squeezenet, @@ -148,16 +170,16 @@ @article{iandola2016squeezenet title = {{SqueezeNet:} {Alexnet-level} accuracy with 50x fewer parameters and 0.5 {MB} model size}, url = {https://arxiv.org/abs/1602.07360}, volume = {abs/1602.07360}, - year = {2016} + year = {2016}, } @misc{intellabs2023knowledge, author = {IntelLabs}, - bdsk-url-1 = {https://intellabs.github.io/distiller/knowledge_distillation.html}, + bdsk-url-1 = {https://intellabs.github.io/distiller/knowledge\_distillation.html}, title = {Knowledge Distillation - Neural Network Distiller}, url = {https://intellabs.github.io/distiller/knowledge_distillation.html}, urldate = {2023-10-20}, - year = {2023} + year = {2023}, } @misc{isscc2014computings, @@ -166,28 +188,35 @@ @misc{isscc2014computings title = {Computing's energy problem (and what we can do about it)}, url = {https://ieeexplore.ieee.org/document/6757323}, urldate = {2014-03-06}, - year = {2014} + year = {2014}, } @misc{jiang2019accuracy, + author = {Hu, Yang and Jiang, Jie and Zhang, Lifu and Shi, Yunfeng and Shi, Jian}, archiveprefix = {arXiv}, - author = {Jiang, Weiwen and Zhang, Xinyi and Sha, Edwin H. -M. and Yang, Lei and Zhuge, Qingfeng and Shi, Yiyu and Hu, Jingtong}, eprint = {1901.11211}, primaryclass = {cs.DC}, - title = {Accuracy vs. Efficiency: {Achieving} Both through {FPGA}-Implementation Aware Neural Architecture Search}, - year = {2019} + title = {Halide Perovskite Semiconductors}, + year = {2023}, + doi = {10.1002/9783527829026.ch13}, + source = {Crossref}, + url = {https://doi.org/10.1002/9783527829026.ch13}, + publisher = {Wiley}, + isbn = {9783527348091, 9783527829026}, + pages = {351--375}, + month = dec, } @inproceedings{jonathan2019lottery, - author = {Jonathan Frankle and Michael Carbin}, + author = {Frankle, Jonathan and Carbin, Michael}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/iclr/FrankleC19.bib}, - booktitle = {7th International Conference on Learning Representations, {ICLR} 2019, New Orleans, LA, USA, May 6-9, 2019}, + booktitle = {7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019}, publisher = {OpenReview.net}, timestamp = {Thu, 25 Jul 2019 01:00:00 +0200}, - title = {The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks}, + title = {The Lottery Ticket Hypothesis: {Finding} Sparse, Trainable Neural Networks}, url = {https://openreview.net/forum?id=rJl-b3RcF7}, - year = {2019} + year = {2019}, } @article{koren2009matrix, @@ -195,19 +224,24 @@ @article{koren2009matrix journal = {Computer}, number = {8}, pages = {30--37}, - publisher = {IEEE}, - title = {Matrix factorization techniques for recommender systems}, + publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, + title = {Matrix Factorization Techniques for Recommender Systems}, volume = {42}, - year = {2009} + year = {2009}, + doi = {10.1109/mc.2009.263}, + source = {Crossref}, + url = {https://doi.org/10.1109/mc.2009.263}, + issn = {0018-9162}, + month = aug, } @misc{krishna2023raman, - archiveprefix = {arXiv}, author = {Krishna, Adithya and Nudurupati, Srikanth Rohit and G, Chandana D and Dwivedi, Pritesh and van Schaik, Andr\'e and Mehendale, Mahesh and Thakur, Chetan Singh}, + archiveprefix = {arXiv}, eprint = {2306.06493}, primaryclass = {cs.NE}, title = {{RAMAN:} {A} Re-configurable and Sparse {TinyML} Accelerator for Inference on Edge}, - year = {2023} + year = {2023}, } @misc{krishnamoorthi2018quantizing, @@ -216,30 +250,30 @@ @misc{krishnamoorthi2018quantizing title = {Quantizing deep convolutional networks for efficient inference: {A} whitepaper}, url = {https://arxiv.org/abs/1806.08342}, volume = {abs/1806.08342}, - year = {2018} + year = {2018}, } @misc{kung2018packing, - archiveprefix = {arXiv}, author = {Kung, H. T. and McDanel, Bradley and Zhang, Sai Qian}, + archiveprefix = {arXiv}, eprint = {1811.04770}, primaryclass = {cs.LG}, title = {Packing Sparse Convolutional Neural Networks for Efficient Systolic Array Implementations: {Column} Combining Under Joint Optimization}, - year = {2018} + year = {2018}, } @misc{kuzmin2022fp8, - archiveprefix = {arXiv}, author = {Kuzmin, Andrey and Baalen, Mart Van and Ren, Yuwei and Nagel, Markus and Peters, Jorn and Blankevoort, Tijmen}, + archiveprefix = {arXiv}, eprint = {2208.09225}, primaryclass = {cs.LG}, title = {{FP8} Quantization: {The} Power of the Exponent}, - year = {2022} + year = {2022}, } @article{kwon2021hardwaresoftware, - article-number = {11073}, author = {Kwon, Jisu and Park, Daejin}, + article-number = {11073}, bdsk-url-1 = {https://www.mdpi.com/2076-3417/11/22/11073}, bdsk-url-2 = {https://doi.org/10.3390/app112211073}, doi = {10.3390/app112211073}, @@ -252,28 +286,29 @@ @article{kwon2021hardwaresoftware title = {{Hardware/Software} Co-Design for {TinyML} Voice-Recognition Application on Resource Frugal Edge Devices}, url = {https://doi.org/10.3390/app112211073}, volume = {11}, - year = {2021} + year = {2021}, + month = nov, } @misc{lai2018cmsisnn, - archiveprefix = {arXiv}, author = {Lai, Liangzhen and Suda, Naveen and Chandra, Vikas}, + archiveprefix = {arXiv}, eprint = {1801.06601}, primaryclass = {cs.NE}, title = {{CMSIS}-{NN:} {Efficient} Neural Network Kernels for Arm Cortex-M {CPUs}}, - year = {2018} + year = {2018}, } @inproceedings{lin2020mcunet, - author = {Ji Lin and Wei{-}Ming Chen and Yujun Lin and John Cohn and Chuang Gan and Song Han}, + author = {Lin, Ji and Chen, Wei-Ming and Lin, Yujun and Cohn, John and Gan, Chuang and Han, Song}, + editor = {Larochelle, Hugo and Ranzato, Marc'Aurelio and Hadsell, Raia and Balcan, Maria-Florina and Lin, Hsuan-Tien}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/LinCLCG020.bib}, booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual}, - editor = {Hugo Larochelle and Marc'Aurelio Ranzato and Raia Hadsell and Maria{-}Florina Balcan and Hsuan{-}Tien Lin}, timestamp = {Thu, 11 Feb 2021 00:00:00 +0100}, - title = {MCUNet: Tiny Deep Learning on IoT Devices}, + title = {{MCUNet:} {Tiny} Deep Learning on {IoT} Devices}, url = {https://proceedings.neurips.cc/paper/2020/hash/86c51678350f656dcc7f490a43946ee5-Abstract.html}, - year = {2020} + year = {2020}, } @misc{lin2023awq, @@ -282,21 +317,26 @@ @misc{lin2023awq title = {{AWQ:} {Activation-aware} Weight Quantization for {LLM} Compression and Acceleration}, url = {https://arxiv.org/abs/2306.00978}, volume = {abs/2306.00978}, - year = {2023} + year = {2023}, } @inproceedings{prakash2022cfu, author = {Prakash, Shvetank and Callahan, Tim and Bushagour, Joseph and Banbury, Colby and Green, Alan V. and Warden, Pete and Ansell, Tim and Reddi, Vijay Janapa}, journal = {ArXiv preprint}, title = {{CFU} Playground: {Full-stack} Open-Source Framework for Tiny Machine Learning {(TinyML)} Acceleration on {FPGAs}}, - url = {https://arxiv.org/abs/2201.01863}, + url = {https://doi.org/10.1109/ispass57527.2023.00024}, volume = {abs/2201.01863}, - year = {2022} + year = {2023}, + doi = {10.1109/ispass57527.2023.00024}, + source = {Crossref}, + booktitle = {2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)}, + publisher = {IEEE}, + month = apr, } @article{qi2021efficient, - abstract = {Nowadays, deep neural networks (DNNs) have been rapidly deployed to realize a number of functionalities like sensing, imaging, classification, recognition, etc. However, the computational-intensive requirement of DNNs makes it difficult to be applicable for resource-limited Internet of Things (IoT) devices. In this paper, we propose a novel pruning-based paradigm that aims to reduce the computational cost of DNNs, by uncovering a more compact structure and learning the effective weights therein, on the basis of not compromising the expressive capability of DNNs. In particular, our algorithm can achieve efficient end-to-end training that transfers a redundant neural network to a compact one with a specifically targeted compression rate directly. We comprehensively evaluate our approach on various representative benchmark datasets and compared with typical advanced convolutional neural network (CNN) architectures. The experimental results verify the superior performance and robust effectiveness of our scheme. For example, when pruning VGG on CIFAR-10, our proposed scheme is able to significantly reduce its FLOPs (floating-point operations) and number of parameters with a proportion of 76.2\% and 94.1\%, respectively, while still maintaining a satisfactory accuracy. To sum up, our scheme could facilitate the integration of DNNs into the common machine-learning-based IoT framework and establish distributed training of neural networks in both cloud and edge.}, author = {Qi, Chen and Shen, Shibo and Li, Rongpeng and Zhao, Zhifeng and Liu, Qing and Liang, Jing and Zhang, Honggang}, + abstract = {Nowadays, deep neural networks (DNNs) have been rapidly deployed to realize a number of functionalities like sensing, imaging, classification, recognition, etc. However, the computational-intensive requirement of DNNs makes it difficult to be applicable for resource-limited Internet of Things (IoT) devices. In this paper, we propose a novel pruning-based paradigm that aims to reduce the computational cost of DNNs, by uncovering a more compact structure and learning the effective weights therein, on the basis of not compromising the expressive capability of DNNs. In particular, our algorithm can achieve efficient end-to-end training that transfers a redundant neural network to a compact one with a specifically targeted compression rate directly. We comprehensively evaluate our approach on various representative benchmark datasets and compared with typical advanced convolutional neural network (CNN) architectures. The experimental results verify the superior performance and robust effectiveness of our scheme. For example, when pruning VGG on CIFAR-10, our proposed scheme is able to significantly reduce its FLOPs (floating-point operations) and number of parameters with a proportion of 76.2\% and 94.1\%, respectively, while still maintaining a satisfactory accuracy. To sum up, our scheme could facilitate the integration of DNNs into the common machine-learning-based IoT framework and establish distributed training of neural networks in both cloud and edge.}, bdsk-url-1 = {https://doi.org/10.1186/s13634-021-00744-4}, doi = {10.1186/s13634-021-00744-4}, file = {Full Text PDF:/Users/jeffreyma/Zotero/storage/AGWCC5VS/Qi et al. - 2021 - An efficient pruning scheme of deep neural network.pdf:application/pdf}, @@ -308,47 +348,50 @@ @article{qi2021efficient title = {An efficient pruning scheme of deep neural networks for Internet of Things applications}, url = {https://doi.org/10.1186/s13634-021-00744-4}, volume = {2021}, - year = {2021} + year = {2021}, + month = jun, } @article{sheng2019qbert, - author = {Sheng Shen and -Zhen Dong and -Jiayu Ye and -Linjian Ma and -Zhewei Yao and -Amir Gholami and -Michael W. Mahoney and -Kurt Keutzer}, + author = {Shen, Sheng and Dong, Zhen and Ye, Jiayu and Ma, Linjian and Yao, Zhewei and Gholami, Amir and Mahoney, Michael W. and Keutzer, Kurt}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/journals/corr/abs-1909-05840.bib}, eprint = {1909.05840}, eprinttype = {arXiv}, - journal = {CoRR}, + journal = {Proceedings of the AAAI Conference on Artificial Intelligence}, timestamp = {Wed, 18 Sep 2019 10:38:36 +0200}, - title = {{Q-BERT:} Hessian Based Ultra Low Precision Quantization of {BERT}}, - url = {http://arxiv.org/abs/1909.05840}, - volume = {abs/1909.05840}, - year = {2019} + title = {Q-{BERT:} {Hessian} Based Ultra Low Precision Quantization of {BERT}}, + url = {https://doi.org/10.1609/aaai.v34i05.6409}, + volume = {34}, + year = {2020}, + doi = {10.1609/aaai.v34i05.6409}, + number = {05}, + source = {Crossref}, + publisher = {Association for the Advancement of Artificial Intelligence (AAAI)}, + issn = {2374-3468, 2159-5399}, + pages = {8815--8821}, + month = apr, } @inproceedings{tan2019mnasnet, - author = {Mingxing Tan and Bo Chen and Ruoming Pang and Vijay Vasudevan and Mark Sandler and Andrew Howard and Quoc V. Le}, + author = {Tan, Mingxing and Chen, Bo and Pang, Ruoming and Vasudevan, Vijay and Sandler, Mark and Howard, Andrew and Le, Quoc V.}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/cvpr/TanCPVSHL19.bib}, - booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2019, Long Beach, CA, USA, June 16-20, 2019}, - doi = {10.1109/CVPR.2019.00293}, + booktitle = {2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, + doi = {10.1109/cvpr.2019.00293}, pages = {2820--2828}, - publisher = {Computer Vision Foundation / {IEEE}}, + publisher = {IEEE}, timestamp = {Tue, 12 Jan 2021 00:00:00 +0100}, - title = {MnasNet: Platform-Aware Neural Architecture Search for Mobile}, - url = {http://openaccess.thecvf.com/content\_CVPR\_2019/html/Tan\_MnasNet\_Platform-Aware\_Neural\_Architecture\_Search\_for\_Mobile\_CVPR\_2019\_paper.html}, - year = {2019} + title = {{MnasNet:} {Platform-aware} Neural Architecture Search for Mobile}, + url = {https://doi.org/10.1109/cvpr.2019.00293}, + year = {2019}, + source = {Crossref}, + month = jun, } @misc{tan2020efficientnet, - archiveprefix = {arXiv}, author = {Tan, Mingxing and Le, Quoc V.}, + archiveprefix = {arXiv}, doi = {10.1002/9781394205639.ch6}, eprint = {1905.11946}, isbn = {9781394205608, 9781394205639}, @@ -358,36 +401,38 @@ @misc{tan2020efficientnet source = {Crossref}, title = {Demystifying Deep Learning}, url = {https://doi.org/10.1002/9781394205639.ch6}, - year = {2023} + year = {2023}, + month = dec, } @misc{ultimate, bdsk-url-1 = {https://deci.ai/quantization-and-quantization-aware-training/}, title = {The Ultimate Guide to Deep Learning Model Quantization and Quantization-Aware Training}, - url = {https://deci.ai/quantization-and-quantization-aware-training/} + url = {https://deci.ai/quantization-and-quantization-aware-training/}, } -@misc{vaswani2023attention, - archiveprefix = {arXiv}, - author = {Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin}, - eprint = {1706.03762}, - primaryclass = {cs.CL}, - title = {Attention Is All You Need}, - year = {2023} +@article{vaswani2017attention, + title={Attention is all you need}, + author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia}, + journal={Advances in neural information processing systems}, + volume={30}, + year={2017} } @inproceedings{wu2019fbnet, - author = {Bichen Wu and Xiaoliang Dai and Peizhao Zhang and Yanghan Wang and Fei Sun and Yiming Wu and Yuandong Tian and Peter Vajda and Yangqing Jia and Kurt Keutzer}, + author = {Wu, Bichen and Keutzer, Kurt and Dai, Xiaoliang and Zhang, Peizhao and Wang, Yanghan and Sun, Fei and Wu, Yiming and Tian, Yuandong and Vajda, Peter and Jia, Yangqing}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/cvpr/WuDZWSWTVJK19.bib}, - booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2019, Long Beach, CA, USA, June 16-20, 2019}, - doi = {10.1109/CVPR.2019.01099}, + booktitle = {2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, + doi = {10.1109/cvpr.2019.01099}, pages = {10734--10742}, - publisher = {Computer Vision Foundation / {IEEE}}, + publisher = {IEEE}, timestamp = {Mon, 20 Jan 2020 00:00:00 +0100}, - title = {FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search}, - url = {http://openaccess.thecvf.com/content\_CVPR\_2019/html/Wu\_FBNet\_Hardware-Aware\_Efficient\_ConvNet\_Design\_via\_Differentiable\_Neural\_Architecture\_Search\_CVPR\_2019\_paper.html}, - year = {2019} + title = {{FBNet:} {Hardware-aware} Efficient {ConvNet} Design via Differentiable Neural Architecture Search}, + url = {https://doi.org/10.1109/cvpr.2019.01099}, + year = {2019}, + source = {Crossref}, + month = jun, } @misc{wu2020integer, @@ -396,7 +441,7 @@ @misc{wu2020integer title = {Integer Quantization for Deep Learning Inference: {Principles} and Empirical Evaluation)}, url = {https://arxiv.org/abs/2004.09602}, volume = {abs/2004.09602}, - year = {2020} + year = {2020}, } @misc{xiao2022smoothquant, @@ -405,30 +450,30 @@ @misc{xiao2022smoothquant title = {{SmoothQuant:} {Accurate} and Efficient Post-Training Quantization for Large Language Models}, url = {https://arxiv.org/abs/2211.10438}, volume = {abs/2211.10438}, - year = {2022} + year = {2022}, } @misc{xinyu, - abstract = {Some simple examples for showing how to use tensor decomposition to reconstruct fluid dynamics}, author = {Xinyu, Chen}, - bdsk-url-1 = {https://medium.com/} + abstract = {Some simple examples for showing how to use tensor decomposition to reconstruct fluid dynamics}, + bdsk-url-1 = {https://medium.com/}, } @inproceedings{xu2018alternating, - author = {Chen Xu and Jianqiang Yao and Zhouchen Lin and Wenwu Ou and Yuanbin Cao and Zhirong Wang and Hongbin Zha}, + author = {Xu, Chen and Yao, Jianqiang and Lin, Zhouchen and Ou, Wenwu and Cao, Yuanbin and Wang, Zhirong and Zha, Hongbin}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/iclr/XuYLOCWZ18.bib}, - booktitle = {6th International Conference on Learning Representations, {ICLR} 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings}, + booktitle = {6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings}, publisher = {OpenReview.net}, timestamp = {Thu, 25 Jul 2019 01:00:00 +0200}, title = {Alternating Multi-bit Quantization for Recurrent Neural Networks}, url = {https://openreview.net/forum?id=S19dR9x0b}, - year = {2018} + year = {2018}, } @misc{yang2020coexploration, - archiveprefix = {arXiv}, author = {Ho Yoon, Jung and Jung, Hyung-Suk and Hwan Lee, Min and Hwan Kim, Gun and Ji Song, Seul and Yeong Seok, Jun and Jean Yoon, Kyung and Seong Hwang, Cheol and Besland, M.-P. and Tranchant, J. and Souchier, E. and Moreau, P. and Salmon, S. and Corraze, B. and Janod, E. and Cario, L. and Zazpe, Ra\'ul and Ungureanu, Mariana and Llopis, Roger and Golmar, Federico and Stoliar, Pablo and Casanova, F\'elix and Eduardo Hueso, Luis and Hermes, C. and Wimmer, M. and Menzel, S. and Fleck, K. and Rana, V. and Salinga, M. and B\"ottger, U. and Bruchhaus, R. and Wuttig, M. and Waser, R. and Lentz, F. and Hermes, C. and R\"osgen, B. and Selle, T. and Bruchhaus, R. and Rana, V. and Waser, R. and Marchewka, Astrid and Menzel, Stephan and B\"ottger, Ulrich and Waser, Rainer and Hoskins, Brian and Alibart, Fabien and Strukov, Dmitri and Pellegrino, Luca and Manca, Nicola and Kanki, Teruo and Tanaka, Hidekazu and Biasotti, Michele and Bellingeri, Emilio and Sergio Siri, Antonio and Marr\'e, Daniele and M. Padilha, Antonio Claudio and Martini Dalpian, Gustavo and Reily Rocha, Alexandre and Prodromakis, Themistoklis and Salaoru, Iulia and Khiat, Ali and Toumazou, Christopher and Gale, Ella M. and Madhavan, A. and Adam, G. and Alibart, F. and Gao, L. and Strukov, D. B. and Wamwangi, D. and Welnic, W. and Wuttig, M. and Gholipour, Behrad and Huang, Chung-Che and Anastasopoulos, Alexandros and Al-Saab, Feras and Hayden, Brian E. and Hewak, Daniel W. and Lan, Rui and Endo, Rie and Kuwahara, Masashi and Kobayashi, Yoshinao and Susa, Masahiro and Baumeister, Paul and Wortmann, Daniel and Bl\"ugel, Stefan and Mazzarello, Riccardo and Li, Yan and Zhang, Wei and Ronneberger, Ider and Simon, Ronnie and Gallus, Jens and Bessas, Dimitrios and Sergueev, Ilya and Wille, Hans-Christian and Pierre Hermann, Rapha\"el and Luckas, Jennifer and Rausch, Pascal and Krebs, Daniel and Zalden, Peter and Boltz, Janika and Raty, Jean-Yves and Salinga, Martin and Longeaud, Christophe and Wuttig, Matthias and Kim, Haeri and Kim, Dong-Wook and Phark, Soo-Hyon and Hong, Seungbum and Park, C. and Herpers, A. and Bruchhaus, R. and Verbeeck, J. and Egoavil, R. and Borgatti, F. and Panaccione, G. and Offi, F. and Dittmann, R. and Clima, Sergiu and Sankaran, Kiroubanand and Mees, Maarten and Yin Chen, Yang and Goux, Ludovic and Govoreanu, Bogdan and Wouters, Dirk J. and Kittl, Jorge and Jurczak, Malgorzata and Pourtois, Geoffrey and Calka, P. and Martinez, E. and Delaye, V. and Lafond, D. and Audoit, G. and Mariolle, D. and Chevalier, N. and Grampeix, H. and Cagli, C. and Jousseaume, V. and Guedj, C. and Shrestha, Pragya and Ochia, Adaku and Cheung, Kin. P. and Campbell, Jason and Baumgart, Helmut and Harris, Gary and Scherff, Malte and Meyer, Bjoern and Scholz, Julius and Hoffmann, Joerg and Jooss, Christian and Xiao, Bo and Tada, Tomofumi and Gu, Tingkun and Tawara, Arihiro and Watanabe, Satoshi and Young, Tai-Fa and Yang, Ya-Liang and Chang, Ting-Chang and Hsu, Kuang-Ting and Chen, Chao-Yu and Burkert, A and Valov, I. and Staikov, G. and Waser, R. and van den Hurk, Jan and Valov, Ilia and Waser, Rainer and Valov, Ilia and Tappertzhofen, Stefan and van der Hurk, Jan and Waser, Rainer and Adam, G. and Alibart, F. and Gao, L. and Hoskins, B. and Strukov, D. B. and Jean Yoon, Kyung and Ji Song, Seul and Kim, Gun Hwan and Seok, Jun Yeong and Ho Yoon, Jeong and Seong Hwang, Cheol and Yoon, Jung Ho and Yoon, Kyung Jin and Shuai, Yao and Wu, Chuangui and Zhang, Wanli and Zhou, Shengqiang and B\"urger, Danilo and Slesazeck, Stefan and Mikolajick, Thomas and Helm, Manfred and Schmidt, Heidemarie and Gale, Ella and Pearson, David and Kitson, Stephen and Adamatzky, Andrew and Costello, Ben de Lacy and Lehtonen, Eero and Poikonen, Jussi and Laiho, Mika and Kanerva, Pentti and Lim, Hyungkwang and Jang, Ho-won and Jeong, Doo Seok and Cao, Xun and Jiang, Meng and Zhang, Feng and Liu, Xinjun and Jin, Ping and Zhang, Kai and Tangirala, Madhavi and Shrestha, Pragya and Baumgart, Helmut and Kittiwatanakul, Salinporn and Lu, Jiwei and Wolf, Stuart and Pallem, Venkateswara and Dussarrat, Christian and Pinto, S. and Krishna, R. and Dias, C. and Pimentel, G. and Oliveira, G. N. P. and Teixeira, J. M. and Aguiar, P. and Titus, E. and Gracio, J. and Ventura, J. and Araujo, J. P.}, + archiveprefix = {arXiv}, doi = {10.1002/9783527667703.ch67}, eprint = {2002.04116}, isbn = {9783527411917, 9783527667703}, @@ -438,20 +483,21 @@ @misc{yang2020coexploration source = {Crossref}, title = {Frontiers in Electronic Materials}, url = {https://doi.org/10.1002/9783527667703.ch67}, - year = {2012} + year = {2012}, + month = jun, } @inproceedings{zhang2019autoshrink, - author = {Tunhou Zhang and Hsin{-}Pai Cheng and Zhenwen Li and Feng Yan and Chengyu Huang and Hai Helen Li and Yiran Chen}, + author = {Zhang, Tunhou and Cheng, Hsin-Pai and Li, Zhenwen and Yan, Feng and Huang, Chengyu and Li, Hai Helen and Chen, Yiran}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/aaai/ZhangCL0HLC20.bib}, - booktitle = {The Thirty-Fourth {AAAI} Conference on Artificial Intelligence, {AAAI} 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, {IAAI} 2020, The Tenth {AAAI} Symposium on Educational Advances in Artificial Intelligence, {EAAI} 2020, New York, NY, USA, February 7-12, 2020}, + booktitle = {The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020}, pages = {6829--6836}, - publisher = {{AAAI} Press}, + publisher = {AAAI Press}, timestamp = {Tue, 02 Feb 2021 00:00:00 +0100}, - title = {AutoShrink: {A} Topology-Aware {NAS} for Discovering Efficient Neural Architecture}, + title = {{AutoShrink:} {A} Topology-Aware {NAS} for Discovering Efficient Neural Architecture}, url = {https://aaai.org/ojs/index.php/AAAI/article/view/6163}, - year = {2020} + year = {2020}, } @inproceedings{zhang2020fast, @@ -462,27 +508,15 @@ @inproceedings{zhang2020fast source = {Crossref}, title = {Fast Hardware-Aware Neural Architecture Search}, url = {https://doi.org/10.1109/cvprw50498.2020.00354}, - year = {2020} + year = {2020}, + month = jun, } @misc{zhou2021analognets, - archiveprefix = {arXiv}, author = {Zhou, Chuteng and Redondo, Fernando Garcia and B\"uchel, Julian and Boybat, Irem and Comas, Xavier Timoneda and Nandakumar, S. R. and Das, Shidhartha and Sebastian, Abu and Gallo, Manuel Le and Whatmough, Paul N.}, + archiveprefix = {arXiv}, eprint = {2111.06503}, primaryclass = {cs.AR}, title = {{AnalogNets:} {Ml-hw} Co-Design of Noise-robust {TinyML} Models and Always-On Analog Compute-in-Memory Accelerator}, - year = {2021} -} - -@article{hawks2021psandqs, - title = {Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference}, - volume = {4}, - ISSN = {2624-8212}, - url = {http://dx.doi.org/10.3389/frai.2021.676564}, - DOI = {10.3389/frai.2021.676564}, - journal = {Frontiers in Artificial Intelligence}, - publisher = {Frontiers Media SA}, - author = {Hawks, Benjamin and Duarte, Javier and Fraser, Nicholas J. and Pappalardo, Alessandro and Tran, Nhan and Umuroglu, Yaman}, year = {2021}, - month = jul -} \ No newline at end of file +} diff --git a/contents/optimizations/optimizations.qmd b/contents/optimizations/optimizations.qmd index 8bb15d42..b822dafe 100644 --- a/contents/optimizations/optimizations.qmd +++ b/contents/optimizations/optimizations.qmd @@ -613,7 +613,7 @@ Upon deciding the type of clipping range, it is essential to tighten the range t ![Illustration of the main forms of quantization granularities. In layerwise quantization, the same clipping range is applied to all filters which belong to the same layer. Notice how this can result in lower quantization resolutions for channels with narrow distributions, e.g. Filter 1, Filter 2, and Filter C. A higher quantization resolution can be achieved using channelwise quantization which dedicates different clipping ranges to different channels (@gholami2021survey).](images/png/efficientnumerics_granularity.png){#fig-quantization-granularity} 1. Layerwise Quantization: This approach determines the clipping range by considering all of the weights in the convolutional filters of a layer. Then, the same clipping range is used for all convolutional filters. It's the simplest to implement, and, as such, it often results in sub-optimal accuracy due the wide variety of differing ranges between filters. For example, a convolutional kernel with a narrower range of parameters loses its quantization resolution due to another kernel in the same layer having a wider range. -2. Groupwise Quantization: This approach groups different channels inside a layer to calculate the clipping range. This method can be helpful when the distribution of parameters across a single convolution/activation varies a lot. In practice, this method was useful in Q-BERT [@sheng2019qbert] for quantizing Transformer [@vaswani2023attention] models that consist of fully-connected attention layers. The downside with this approach comes with the extra cost of accounting for different scaling factors. +2. Groupwise Quantization: This approach groups different channels inside a layer to calculate the clipping range. This method can be helpful when the distribution of parameters across a single convolution/activation varies a lot. In practice, this method was useful in Q-BERT [@sheng2019qbert] for quantizing Transformer [@vaswani2017attention] models that consist of fully-connected attention layers. The downside with this approach comes with the extra cost of accounting for different scaling factors. 3. Channelwise Quantization: This popular method uses a fixed range for each convolutional filter that is independent of other channels. Because each channel is assigned a dedicated scaling factor, this method ensures a higher quantization resolution and often results in higher accuracy. 4. Sub-channelwise Quantization: Taking channelwise quantization to the extreme, this method determines the clipping range with respect to any groups of parameters in a convolution or fully-connected layer. It may result in considerable overhead since different scaling factors need to be taken into account when processing a single convolution or fully-connected layer. diff --git a/contents/privacy_security/privacy_security.bib b/contents/privacy_security/privacy_security.bib index caca7414..2a3edc3e 100644 --- a/contents/privacy_security/privacy_security.bib +++ b/contents/privacy_security/privacy_security.bib @@ -1,6 +1,184 @@ +%comment{This file was created with betterbib v5.0.11.} + + +@inproceedings{Asonov2004Keyboard, + author = {Asonov, D. and Agrawal, R.}, + booktitle = {IEEE Symposium on Security and Privacy, 2004. Proceedings. 2004}, + date-added = {2023-11-22 17:05:39 -0500}, + date-modified = {2023-11-22 17:06:45 -0500}, + doi = {10.1109/secpri.2004.1301311}, + organization = {IEEE}, + pages = {3--11}, + publisher = {IEEE}, + source = {Crossref}, + title = {Keyboard acoustic emanations}, + url = {https://doi.org/10.1109/secpri.2004.1301311}, + year = {2004}, +} + +@inproceedings{Biega2020Oper, + author = {Biega, Asia J. and Potash, Peter and Daum\'e, Hal and Diaz, Fernando and Finck, Mich\`ele}, + editor = {Huang, Jimmy and Chang, Yi and Cheng, Xueqi and Kamps, Jaap and Murdock, Vanessa and Wen, Ji-Rong and Liu, Yiqun}, + bibsource = {dblp computer science bibliography, https://dblp.org}, + biburl = {https://dblp.org/rec/conf/sigir/BiegaPDDF20.bib}, + booktitle = {Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval}, + doi = {10.1145/3397271.3401034}, + pages = {399--408}, + publisher = {ACM}, + timestamp = {Thu, 03 Sep 2020 01:00:00 +0200}, + title = {Operationalizing the Legal Principle of Data Minimization for Personalization}, + url = {https://doi.org/10.1145/3397271.3401034}, + year = {2020}, + source = {Crossref}, + month = jul, +} + +@article{Burnet1989Spycatcher, + author = {Burnet, David and Thomas, Richard}, + date-added = {2023-11-22 17:03:00 -0500}, + date-modified = {2023-11-22 17:04:44 -0500}, + doi = {10.2307/1410360}, + issn = {0263-323X}, + journal = {J. Law Soc.}, + number = {2}, + pages = {210}, + publisher = {JSTOR}, + source = {Crossref}, + title = {Spycatcher: {The} Commodification of Truth}, + url = {https://doi.org/10.2307/1410360}, + volume = {16}, + year = {1989}, +} + +@inproceedings{Dwork2006Theory, + author = {Dwork, Cynthia and McSherry, Frank and Nissim, Kobbi and Smith, Adam}, + editor = {Halevi, Shai and Rabin, Tal}, + address = {Berlin, Heidelberg}, + booktitle = {Theory of Cryptography}, + date-added = {2023-11-22 18:04:12 -0500}, + date-modified = {2023-11-22 18:05:20 -0500}, + pages = {265--284}, + publisher = {Springer Berlin Heidelberg}, + title = {Calibrating Noise to Sensitivity in Private Data Analysis}, + year = {2006}, +} + +@article{Gao2020Physical, + author = {Gao, Yansong and Al-Sarawi, Said F. and Abbott, Derek}, + date-added = {2023-11-22 17:52:20 -0500}, + date-modified = {2023-11-22 17:54:56 -0500}, + doi = {10.1038/s41928-020-0372-5}, + issn = {2520-1131}, + journal = {Nature Electronics}, + number = {2}, + pages = {81--91}, + publisher = {Springer Science and Business Media LLC}, + source = {Crossref}, + title = {Physical unclonable functions}, + url = {https://doi.org/10.1038/s41928-020-0372-5}, + volume = {3}, + year = {2020}, + month = feb, +} + +@article{Gupta2023ChatGPT, + author = {Gupta, Maanak and Akiri, Charankumar and Aryal, Kshitiz and Parker, Eli and Praharaj, Lopamudra}, + date-added = {2023-11-22 18:01:41 -0500}, + date-modified = {2023-11-22 18:02:55 -0500}, + doi = {10.1109/access.2023.3300381}, + issn = {2169-3536}, + journal = {\#IEEE\_O\_ACC\#}, + pages = {80218--80245}, + publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, + source = {Crossref}, + title = {From {ChatGPT} to {ThreatGPT:} {Impact} of Generative {AI} in Cybersecurity and Privacy}, + url = {https://doi.org/10.1109/access.2023.3300381}, + volume = {11}, + year = {2023}, +} + +@article{Kocher2011Intro, + author = {Kocher, Paul and Jaffe, Joshua and Jun, Benjamin and Rohatgi, Pankaj}, + date-added = {2023-11-22 16:58:42 -0500}, + date-modified = {2023-11-22 17:00:36 -0500}, + doi = {10.1007/s13389-011-0006-y}, + issn = {2190-8508, 2190-8516}, + journal = {Journal of Cryptographic Engineering}, + number = {1}, + pages = {5--27}, + publisher = {Springer Science and Business Media LLC}, + source = {Crossref}, + title = {Introduction to differential power analysis}, + url = {https://doi.org/10.1007/s13389-011-0006-y}, + volume = {1}, + year = {2011}, + month = mar, +} + +@inproceedings{Kocher2018spectre, + author = {Kocher, Paul and Horn, Jann and Fogh, Anders and Genkin, Daniel and Gruss, Daniel and Haas, Werner and Hamburg, Mike and Lipp, Moritz and Mangard, Stefan and Prescher, Thomas and Schwarz, Michael and Yarom, Yuval}, + booktitle = {2019 IEEE Symposium on Security and Privacy (SP)}, + date-added = {2023-11-22 16:33:35 -0500}, + date-modified = {2023-11-22 16:34:01 -0500}, + doi = {10.1109/sp.2019.00002}, + publisher = {IEEE}, + source = {Crossref}, + title = {Spectre Attacks: {Exploiting} Speculative Execution}, + url = {https://doi.org/10.1109/sp.2019.00002}, + year = {2019}, + month = may, +} + +@article{Li2020Federated, + author = {Li, Tian and Sahu, Anit Kumar and Talwalkar, Ameet and Smith, Virginia}, + date-added = {2023-11-22 19:15:13 -0500}, + date-modified = {2023-11-22 19:17:19 -0500}, + doi = {10.1109/msp.2020.2975749}, + issn = {1053-5888, 1558-0792}, + journal = {IEEE Signal Process Mag.}, + number = {3}, + pages = {50--60}, + publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, + source = {Crossref}, + title = {Federated Learning: {Challenges,} Methods, and Future Directions}, + url = {https://doi.org/10.1109/msp.2020.2975749}, + volume = {37}, + year = {2020}, + month = may, +} + +@inproceedings{Lipp2018meltdown, + author = {Kocher, Paul and Horn, Jann and Fogh, Anders and Genkin, Daniel and Gruss, Daniel and Haas, Werner and Hamburg, Mike and Lipp, Moritz and Mangard, Stefan and Prescher, Thomas and Schwarz, Michael and Yarom, Yuval}, + booktitle = {2019 IEEE Symposium on Security and Privacy (SP)}, + date-added = {2023-11-22 16:32:26 -0500}, + date-modified = {2023-11-22 16:33:08 -0500}, + doi = {10.1109/sp.2019.00002}, + publisher = {IEEE}, + source = {Crossref}, + title = {Spectre Attacks: {Exploiting} Speculative Execution}, + url = {https://doi.org/10.1109/sp.2019.00002}, + year = {2019}, + month = may, +} + +@inproceedings{Rashmi2018Secure, + author = {R.V., Rashmi and A., Karthikeyan}, + booktitle = {2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA)}, + date-added = {2023-11-22 17:50:16 -0500}, + date-modified = {2023-11-22 17:51:39 -0500}, + doi = {10.1109/iceca.2018.8474730}, + pages = {291--298}, + publisher = {IEEE}, + source = {Crossref}, + title = {Secure boot of Embedded Applications - A Review}, + url = {https://doi.org/10.1109/iceca.2018.8474730}, + year = {2018}, + month = mar, +} + @inproceedings{abadi2016deep, - address = {New York, NY, USA}, author = {Abadi, Martin and Chu, Andy and Goodfellow, Ian and McMahan, H. Brendan and Mironov, Ilya and Talwar, Kunal and Zhang, Li}, + address = {New York, NY, USA}, booktitle = {Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security}, date-added = {2023-11-22 18:06:03 -0500}, date-modified = {2023-11-22 18:08:42 -0500}, @@ -12,7 +190,8 @@ @inproceedings{abadi2016deep source = {Crossref}, title = {Deep Learning with Differential Privacy}, url = {https://doi.org/10.1145/2976749.2978318}, - year = {2016} + year = {2016}, + month = oct, } @inproceedings{agrawal2003side, @@ -25,21 +204,24 @@ @inproceedings{agrawal2003side source = {Crossref}, title = {{Trojan} Detection using {IC} Fingerprinting}, url = {https://doi.org/10.1109/sp.2007.36}, - year = {2007} + year = {2007}, + month = may, } @inproceedings{ahmed2020headless, - author = {Ahmed Abdelkader and Michael J. Curry and Liam Fowl and Tom Goldstein and Avi Schwarzschild and Manli Shu and Christoph Studer and Chen Zhu}, + author = {Abdelkader, Ahmed and Curry, Michael J. and Fowl, Liam and Goldstein, Tom and Schwarzschild, Avi and Shu, Manli and Studer, Christoph and Zhu, Chen}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/icassp/AbdelkaderCFGSS20.bib}, - booktitle = {2020 {IEEE} International Conference on Acoustics, Speech and Signal Processing, {ICASSP} 2020, Barcelona, Spain, May 4-8, 2020}, - doi = {10.1109/ICASSP40776.2020.9053181}, + booktitle = {ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, + doi = {10.1109/icassp40776.2020.9053181}, pages = {3087--3091}, - publisher = {{IEEE}}, + publisher = {IEEE}, timestamp = {Thu, 23 Jul 2020 01:00:00 +0200}, - title = {Headless Horseman: Adversarial Attacks on Transfer Learning Models}, - url = {https://doi.org/10.1109/ICASSP40776.2020.9053181}, - year = {2020} + title = {Headless Horseman: {Adversarial} Attacks on Transfer Learning Models}, + url = {https://doi.org/10.1109/icassp40776.2020.9053181}, + year = {2020}, + source = {Crossref}, + month = may, } @inproceedings{amiel2006fault, @@ -50,7 +232,7 @@ @inproceedings{amiel2006fault organization = {Springer}, pages = {223--236}, title = {Fault analysis of {DPA}-resistant algorithms}, - year = {2006} + year = {2006}, } @inproceedings{antonakakis2017understanding, @@ -58,22 +240,7 @@ @inproceedings{antonakakis2017understanding booktitle = {26th USENIX security symposium (USENIX Security 17)}, pages = {1093--1110}, title = {Understanding the mirai botnet}, - year = {2017} -} - -@inproceedings{Asonov2004Keyboard, - author = {Asonov, D. and Agrawal, R.}, - booktitle = {IEEE Symposium on Security and Privacy, 2004. Proceedings. 2004}, - date-added = {2023-11-22 17:05:39 -0500}, - date-modified = {2023-11-22 17:06:45 -0500}, - doi = {10.1109/secpri.2004.1301311}, - organization = {IEEE}, - pages = {3--11}, - publisher = {IEEE}, - source = {Crossref}, - title = {Keyboard acoustic emanations}, - url = {https://doi.org/10.1109/secpri.2004.1301311}, - year = {2004} + year = {2017}, } @article{ateniese2015hacking, @@ -90,7 +257,7 @@ @article{ateniese2015hacking title = {Hacking smart machines with smarter ones: {How} to extract meaningful data from machine learning classifiers}, url = {https://doi.org/10.1504/ijsn.2015.071829}, volume = {10}, - year = {2015} + year = {2015}, } @inproceedings{barenghi2010low, @@ -105,34 +272,20 @@ @inproceedings{barenghi2010low source = {Crossref}, title = {Low voltage fault attacks to {AES}}, url = {https://doi.org/10.1109/hst.2010.5513121}, - year = {2010} -} - -@inproceedings{Biega2020Oper, - author = {Asia J. Biega and Peter Potash and Hal Daum{\'{e}} III and Fernando Diaz and Mich{\`{e}}le Finck}, - bibsource = {dblp computer science bibliography, https://dblp.org}, - biburl = {https://dblp.org/rec/conf/sigir/BiegaPDDF20.bib}, - booktitle = {Proceedings of the 43rd International {ACM} {SIGIR} conference on research and development in Information Retrieval, {SIGIR} 2020, Virtual Event, China, July 25-30, 2020}, - doi = {10.1145/3397271.3401034}, - editor = {Jimmy Huang and Yi Chang and Xueqi Cheng and Jaap Kamps and Vanessa Murdock and Ji{-}Rong Wen and Yiqun Liu}, - pages = {399--408}, - publisher = {{ACM}}, - timestamp = {Thu, 03 Sep 2020 01:00:00 +0200}, - title = {Operationalizing the Legal Principle of Data Minimization for Personalization}, - url = {https://doi.org/10.1145/3397271.3401034}, - year = {2020} + year = {2010}, + month = jun, } @inproceedings{biggio2012poisoning, - author = {Battista Biggio and Blaine Nelson and Pavel Laskov}, + author = {Biggio, Battista and Nelson, Blaine and Laskov, Pavel}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/icml/BiggioNL12.bib}, - booktitle = {Proceedings of the 29th International Conference on Machine Learning, {ICML} 2012, Edinburgh, Scotland, UK, June 26 - July 1, 2012}, + booktitle = {Proceedings of the 29th International Conference on Machine Learning, ICML 2012, Edinburgh, Scotland, UK, June 26 - July 1, 2012}, publisher = {icml.cc / Omnipress}, timestamp = {Wed, 03 Apr 2019 01:00:00 +0200}, title = {Poisoning Attacks against Support Vector Machines}, url = {http://icml.cc/2012/papers/880.pdf}, - year = {2012} + year = {2012}, } @article{breier2018deeplaser, @@ -141,24 +294,7 @@ @article{breier2018deeplaser title = {Deeplaser: {Practical} fault attack on deep neural networks}, url = {https://arxiv.org/abs/1806.05859}, volume = {abs/1806.05859}, - year = {2018} -} - -@article{Burnet1989Spycatcher, - author = {Burnet, David and Thomas, Richard}, - date-added = {2023-11-22 17:03:00 -0500}, - date-modified = {2023-11-22 17:04:44 -0500}, - doi = {10.2307/1410360}, - issn = {0263-323X}, - journal = {J. Law Soc.}, - number = {2}, - pages = {210}, - publisher = {JSTOR}, - source = {Crossref}, - title = {Spycatcher: {The} Commodification of Truth}, - url = {https://doi.org/10.2307/1410360}, - volume = {16}, - year = {1989} + year = {2018}, } @article{cavoukian2009privacy, @@ -167,7 +303,7 @@ @article{cavoukian2009privacy date-modified = {2023-11-22 17:56:58 -0500}, journal = {Office of the Information and Privacy Commissioner}, title = {Privacy by design}, - year = {2009} + year = {2009}, } @book{dhanjani2015abusing, @@ -180,20 +316,7 @@ @book{dhanjani2015abusing source = {Crossref}, title = {The Internet of Things}, url = {https://doi.org/10.7551/mitpress/10277.001.0001}, - year = {2015} -} - -@inproceedings{Dwork2006Theory, - address = {Berlin, Heidelberg}, - author = {Dwork, Cynthia and McSherry, Frank and Nissim, Kobbi and Smith, Adam}, - booktitle = {Theory of Cryptography}, - date-added = {2023-11-22 18:04:12 -0500}, - date-modified = {2023-11-22 18:05:20 -0500}, - editor = {Halevi, Shai and Rabin, Tal}, - pages = {265--284}, - publisher = {Springer Berlin Heidelberg}, - title = {Calibrating Noise to Sensitivity in Private Data Analysis}, - year = {2006} + year = {2015}, } @article{eldan2023whos, @@ -202,7 +325,7 @@ @article{eldan2023whos title = {Who's Harry Potter? Approximate Unlearning in {LLMs}}, url = {https://arxiv.org/abs/2310.02238}, volume = {abs/2310.02238}, - year = {2023} + year = {2023}, } @article{eykholt2018robust, @@ -211,7 +334,7 @@ @article{eykholt2018robust title = {Robust Physical-World Attacks on Deep Learning Models}, url = {https://arxiv.org/abs/1707.08945}, volume = {abs/1707.08945}, - year = {2017} + year = {2017}, } @article{farwell2011stuxnet, @@ -228,7 +351,8 @@ @article{farwell2011stuxnet title = {Stuxnet and the Future of Cyber War}, url = {https://doi.org/10.1080/00396338.2011.555586}, volume = {53}, - year = {2011} + year = {2011}, + month = jan, } @inproceedings{gandolfi2001electromagnetic, @@ -239,24 +363,7 @@ @inproceedings{gandolfi2001electromagnetic organization = {Springer}, pages = {251--261}, title = {Electromagnetic analysis: {Concrete} results}, - year = {2001} -} - -@article{Gao2020Physical, - author = {Gao, Yansong and Al-Sarawi, Said F. and Abbott, Derek}, - date-added = {2023-11-22 17:52:20 -0500}, - date-modified = {2023-11-22 17:54:56 -0500}, - doi = {10.1038/s41928-020-0372-5}, - issn = {2520-1131}, - journal = {Nature Electronics}, - number = {2}, - pages = {81--91}, - publisher = {Springer Science and Business Media LLC}, - source = {Crossref}, - title = {Physical unclonable functions}, - url = {https://doi.org/10.1038/s41928-020-0372-5}, - volume = {3}, - year = {2020} + year = {2001}, } @inproceedings{gnad2017voltage, @@ -271,7 +378,8 @@ @inproceedings{gnad2017voltage source = {Crossref}, title = {Voltage drop-based fault attacks on {FPGAs} using valid bitstreams}, url = {https://doi.org/10.23919/fpl.2017.8056840}, - year = {2017} + year = {2017}, + month = sep, } @article{goodfellow2020generative, @@ -286,23 +394,8 @@ @article{goodfellow2020generative title = {Generative adversarial networks}, url = {https://doi.org/10.1145/3422622}, volume = {63}, - year = {2020} -} - -@article{Gupta2023ChatGPT, - author = {Gupta, Maanak and Akiri, Charankumar and Aryal, Kshitiz and Parker, Eli and Praharaj, Lopamudra}, - date-added = {2023-11-22 18:01:41 -0500}, - date-modified = {2023-11-22 18:02:55 -0500}, - doi = {10.1109/access.2023.3300381}, - issn = {2169-3536}, - journal = {\#IEEE\_O\_ACC\#}, - pages = {80218--80245}, - publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, - source = {Crossref}, - title = {From {ChatGPT} to {ThreatGPT:} {Impact} of Generative {AI} in Cybersecurity and Privacy}, - url = {https://doi.org/10.1109/access.2023.3300381}, - volume = {11}, - year = {2023} + year = {2020}, + month = oct, } @article{hosseini2017deceiving, @@ -311,7 +404,7 @@ @article{hosseini2017deceiving title = {Deceiving google's perspective api built for detecting toxic comments}, url = {https://arxiv.org/abs/1702.08138}, volume = {abs/1702.08138}, - year = {2017} + year = {2017}, } @inproceedings{hsiao2023mavfi, @@ -326,7 +419,8 @@ @inproceedings{hsiao2023mavfi source = {Crossref}, title = {{MAVFI:} {An} End-to-End Fault Analysis Framework with Anomaly Detection and Recovery for Micro Aerial Vehicles}, url = {https://doi.org/10.23919/date56975.2023.10137246}, - year = {2023} + year = {2023}, + month = apr, } @inproceedings{hutter2009contact, @@ -341,7 +435,15 @@ @inproceedings{hutter2009contact source = {Crossref}, title = {Contact-based fault injections and power analysis on {RFID} tags}, url = {https://doi.org/10.1109/ecctd.2009.5275012}, - year = {2009} + year = {2009}, + month = aug, +} + +@article{jin2020towards, + author = {Jin, Yilun and Wei, Xiguang and Liu, Yang and Yang, Qiang}, + title = {Towards utilizing unlabeled data in federated learning: {A} survey and prospective}, + journal = {arXiv preprint arXiv:2002.11545}, + year = {2020}, } @book{joye2012fault, @@ -355,7 +457,7 @@ @book{joye2012fault source = {Crossref}, title = {Fault Analysis in Cryptography}, url = {https://doi.org/10.1007/978-3-642-29656-7}, - year = {2012} + year = {2012}, } @article{kairouz2021advances, @@ -373,20 +475,20 @@ @article{kairouz2021advances title = {Advances and Open Problems in Federated Learning}, url = {https://doi.org/10.1561/2200000083}, volume = {14}, - year = {2021} + year = {2021}, } @inproceedings{khan2021knowledgeadaptation, - author = {Mohammad Emtiyaz Khan and Siddharth Swaroop}, + author = {Khan, Mohammad Emtiyaz and Swaroop, Siddharth}, + editor = {Ranzato, Marc'Aurelio and Beygelzimer, Alina and Dauphin, Yann N. and Liang, Percy and Vaughan, Jennifer Wortman}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/KhanS21.bib}, booktitle = {Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual}, - editor = {Marc'Aurelio Ranzato and Alina Beygelzimer and Yann N. Dauphin and Percy Liang and Jennifer Wortman Vaughan}, pages = {19757--19770}, timestamp = {Tue, 03 May 2022 01:00:00 +0200}, title = {Knowledge-Adaptation Priors}, url = {https://proceedings.neurips.cc/paper/2021/hash/a4380923dd651c195b1631af7c829187-Abstract.html}, - year = {2021} + year = {2021}, } @inproceedings{kocher1999differential, @@ -397,67 +499,7 @@ @inproceedings{kocher1999differential organization = {Springer}, pages = {388--397}, title = {Differential power analysis}, - year = {1999} -} - -@article{Kocher2011Intro, - author = {Kocher, Paul and Jaffe, Joshua and Jun, Benjamin and Rohatgi, Pankaj}, - date-added = {2023-11-22 16:58:42 -0500}, - date-modified = {2023-11-22 17:00:36 -0500}, - doi = {10.1007/s13389-011-0006-y}, - issn = {2190-8508, 2190-8516}, - journal = {Journal of Cryptographic Engineering}, - number = {1}, - pages = {5--27}, - publisher = {Springer Science and Business Media LLC}, - source = {Crossref}, - title = {Introduction to differential power analysis}, - url = {https://doi.org/10.1007/s13389-011-0006-y}, - volume = {1}, - year = {2011} -} - -@inproceedings{Kocher2018spectre, - author = {Kocher, Paul and Horn, Jann and Fogh, Anders and Genkin, Daniel and Gruss, Daniel and Haas, Werner and Hamburg, Mike and Lipp, Moritz and Mangard, Stefan and Prescher, Thomas and Schwarz, Michael and Yarom, Yuval}, - booktitle = {2019 IEEE Symposium on Security and Privacy (SP)}, - date-added = {2023-11-22 16:33:35 -0500}, - date-modified = {2023-11-22 16:34:01 -0500}, - doi = {10.1109/sp.2019.00002}, - publisher = {IEEE}, - source = {Crossref}, - title = {Spectre Attacks: {Exploiting} Speculative Execution}, - url = {https://doi.org/10.1109/sp.2019.00002}, - year = {2019} -} - -@article{Li2020Federated, - author = {Li, Tian and Sahu, Anit Kumar and Talwalkar, Ameet and Smith, Virginia}, - date-added = {2023-11-22 19:15:13 -0500}, - date-modified = {2023-11-22 19:17:19 -0500}, - doi = {10.1109/msp.2020.2975749}, - issn = {1053-5888, 1558-0792}, - journal = {IEEE Signal Process Mag.}, - number = {3}, - pages = {50--60}, - publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, - source = {Crossref}, - title = {Federated Learning: {Challenges,} Methods, and Future Directions}, - url = {https://doi.org/10.1109/msp.2020.2975749}, - volume = {37}, - year = {2020} -} - -@inproceedings{Lipp2018meltdown, - author = {Kocher, Paul and Horn, Jann and Fogh, Anders and Genkin, Daniel and Gruss, Daniel and Haas, Werner and Hamburg, Mike and Lipp, Moritz and Mangard, Stefan and Prescher, Thomas and Schwarz, Michael and Yarom, Yuval}, - booktitle = {2019 IEEE Symposium on Security and Privacy (SP)}, - date-added = {2023-11-22 16:32:26 -0500}, - date-modified = {2023-11-22 16:33:08 -0500}, - doi = {10.1109/sp.2019.00002}, - publisher = {IEEE}, - source = {Crossref}, - title = {Spectre Attacks: {Exploiting} Speculative Execution}, - url = {https://doi.org/10.1109/sp.2019.00002}, - year = {2019} + year = {1999}, } @article{miller2015remote, @@ -469,7 +511,7 @@ @article{miller2015remote pages = {1--91}, title = {Remote exploitation of an unaltered passenger vehicle}, volume = {2015}, - year = {2015} + year = {2015}, } @article{miller2019lessons, @@ -486,7 +528,8 @@ @article{miller2019lessons title = {Lessons learned from hacking a car}, url = {https://doi.org/10.1109/mdat.2018.2863106}, volume = {36}, - year = {2019} + year = {2019}, + month = dec, } @article{narayanan2006break, @@ -495,7 +538,7 @@ @article{narayanan2006break date-modified = {2023-11-22 16:16:59 -0500}, journal = {arXiv preprint cs/0610105}, title = {How to break anonymity of the netflix prize dataset}, - year = {2006} + year = {2006}, } @article{oliynyk2023know, @@ -513,7 +556,8 @@ @article{oliynyk2023know title = {I Know What You Trained Last Summer: {A} Survey on Stealing Machine Learning Models and Defences}, url = {https://doi.org/10.1145/3595292}, volume = {55}, - year = {2023} + year = {2023}, + month = jul, } @article{oprea2022poisoning, @@ -528,7 +572,8 @@ @article{oprea2022poisoning title = {Poisoning Attacks Against Machine Learning: {Can} Machine Learning Be Trustworthy?}, url = {https://doi.org/10.1109/mc.2022.3190787}, volume = {55}, - year = {2022} + year = {2022}, + month = nov, } @article{parrish2023adversarial, @@ -537,37 +582,23 @@ @article{parrish2023adversarial title = {Adversarial Nibbler: {A} Data-Centric Challenge for Improving the Safety of Text-to-Image Models}, url = {https://arxiv.org/abs/2305.14384}, volume = {abs/2305.14384}, - year = {2023} + year = {2023}, } @inproceedings{ramesh2021zero, - author = {Aditya Ramesh and Mikhail Pavlov and Gabriel Goh and Scott Gray and Chelsea Voss and Alec Radford and Mark Chen and Ilya Sutskever}, + author = {Ramesh, Aditya and Pavlov, Mikhail and Goh, Gabriel and Gray, Scott and Voss, Chelsea and Radford, Alec and Chen, Mark and Sutskever, Ilya}, + editor = {Meila, Marina and Zhang, Tong}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/icml/RameshPGGVRCS21.bib}, - booktitle = {Proceedings of the 38th International Conference on Machine Learning, {ICML} 2021, 18-24 July 2021, Virtual Event}, - editor = {Marina Meila and Tong Zhang}, + booktitle = {Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event}, pages = {8821--8831}, - publisher = {{PMLR}}, + publisher = {PMLR}, series = {Proceedings of Machine Learning Research}, timestamp = {Wed, 25 Aug 2021 01:00:00 +0200}, title = {Zero-Shot Text-to-Image Generation}, url = {http://proceedings.mlr.press/v139/ramesh21a.html}, volume = {139}, - year = {2021} -} - -@inproceedings{Rashmi2018Secure, - author = {R.V., Rashmi and A., Karthikeyan}, - booktitle = {2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA)}, - date-added = {2023-11-22 17:50:16 -0500}, - date-modified = {2023-11-22 17:51:39 -0500}, - doi = {10.1109/iceca.2018.8474730}, - pages = {291--298}, - publisher = {IEEE}, - source = {Crossref}, - title = {Secure boot of Embedded Applications - A Review}, - url = {https://doi.org/10.1109/iceca.2018.8474730}, - year = {2018} + year = {2021}, } @inproceedings{rombach2022highresolution, @@ -578,7 +609,23 @@ @inproceedings{rombach2022highresolution source = {Crossref}, title = {High-Resolution Image Synthesis with Latent Diffusion Models}, url = {https://doi.org/10.1109/cvpr52688.2022.01042}, - year = {2022} + year = {2022}, + month = jun, +} + +@article{rosa2021, + author = {de Rosa, Gustavo H. and Papa, Jo\~ao P.}, + journal = {Pattern Recogn.}, + title = {A survey on text generation using generative adversarial networks}, + year = {2021}, + doi = {10.1016/j.patcog.2021.108098}, + source = {Crossref}, + url = {https://doi.org/10.1016/j.patcog.2021.108098}, + volume = {119}, + publisher = {Elsevier BV}, + issn = {0031-3203}, + pages = {108098}, + month = nov, } @article{shan2023prompt, @@ -587,7 +634,7 @@ @article{shan2023prompt title = {Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models}, url = {https://arxiv.org/abs/2310.13828}, volume = {abs/2310.13828}, - year = {2023} + year = {2023}, } @inproceedings{skorobogatov2003optical, @@ -596,7 +643,7 @@ @inproceedings{skorobogatov2003optical organization = {Springer}, pages = {2--12}, title = {Optical fault induction attacks}, - year = {2003} + year = {2003}, } @inproceedings{skorobogatov2009local, @@ -609,7 +656,7 @@ @inproceedings{skorobogatov2009local source = {Crossref}, title = {Local heating attacks on Flash memory devices}, url = {https://doi.org/10.1109/hst.2009.5225028}, - year = {2009} + year = {2009}, } @article{tarun2023deep, @@ -618,7 +665,7 @@ @article{tarun2023deep title = {Deep Regression Unlearning}, url = {https://arxiv.org/abs/2210.08196}, volume = {abs/2210.08196}, - year = {2022} + year = {2022}, } @inproceedings{zhao2018fpga, @@ -633,20 +680,6 @@ @inproceedings{zhao2018fpga source = {Crossref}, title = {{FPGA}-Based Remote Power Side-Channel Attacks}, url = {https://doi.org/10.1109/sp.2018.00049}, - year = {2018} + year = {2018}, + month = may, } - -@article{rosa2021, - author = {G. H. de Rosa and J. P. Papa}, - journal = {Pattern Recognition}, - title = {A survey on text generation using generative adversarial networks}, - year = {2021}, - doi = {10.1016/j.patcog.2021.108098} -} - -@article{jin2020towards, - title={Towards utilizing unlabeled data in federated learning: A survey and prospective}, - author={Jin, Yilun and Wei, Xiguang and Liu, Yang and Yang, Qiang}, - journal={arXiv preprint arXiv:2002.11545}, - year={2020} -} \ No newline at end of file diff --git a/contents/responsible_ai/responsible_ai.bib b/contents/responsible_ai/responsible_ai.bib index 10c3d377..7e75cce4 100644 --- a/contents/responsible_ai/responsible_ai.bib +++ b/contents/responsible_ai/responsible_ai.bib @@ -1,6 +1,9 @@ +%comment{This file was created with betterbib v5.0.11.} + + @inproceedings{abadi2016deep, - address = {New York, NY, USA}, author = {Abadi, Martin and Chu, Andy and Goodfellow, Ian and McMahan, H. Brendan and Mironov, Ilya and Talwar, Kunal and Zhang, Li}, + address = {New York, NY, USA}, booktitle = {Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security}, date-added = {2023-11-22 18:06:03 -0500}, date-modified = {2023-11-22 18:08:42 -0500}, @@ -12,23 +15,24 @@ @inproceedings{abadi2016deep source = {Crossref}, title = {Deep Learning with Differential Privacy}, url = {https://doi.org/10.1145/2976749.2978318}, - year = {2016} + year = {2016}, + month = oct, } @inproceedings{agarwal2018reductions, - author = {Alekh Agarwal and Alina Beygelzimer and Miroslav Dud{\'{\i}}k and John Langford and Hanna M. Wallach}, + author = {Agarwal, Alekh and Beygelzimer, Alina and Dud{\'\i}k, Miroslav and Langford, John and Wallach, Hanna M.}, + editor = {Dy, Jennifer G. and Krause, Andreas}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/icml/AgarwalBD0W18.bib}, - booktitle = {Proceedings of the 35th International Conference on Machine Learning, {ICML} 2018, Stockholmsm{\"{a}}ssan, Stockholm, Sweden, July 10-15, 2018}, - editor = {Jennifer G. Dy and Andreas Krause}, + booktitle = {Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm\"assan, Stockholm, Sweden, July 10-15, 2018}, pages = {60--69}, - publisher = {{PMLR}}, + publisher = {PMLR}, series = {Proceedings of Machine Learning Research}, timestamp = {Wed, 03 Apr 2019 01:00:00 +0200}, title = {A Reductions Approach to Fair Classification}, url = {http://proceedings.mlr.press/v80/agarwal18a.html}, volume = {80}, - year = {2018} + year = {2018}, } @article{alghamdi2022beyond, @@ -37,7 +41,7 @@ @article{alghamdi2022beyond pages = {38747--38760}, title = {Beyond Adult and {COMPAS:} {Fair} multi-class prediction via information projection}, volume = {35}, - year = {2022} + year = {2022}, } @article{amodei2016concrete, @@ -52,21 +56,24 @@ @article{amodei2016concrete title = {{ArXiv} preprint server plans multimillion-dollar overhaul}, url = {https://doi.org/10.1038/534602a}, volume = {534}, - year = {2016} + year = {2016}, + month = jun, } @inproceedings{bau2017network, - author = {David Bau and Bolei Zhou and Aditya Khosla and Aude Oliva and Antonio Torralba}, + author = {Bau, David and Zhou, Bolei and Khosla, Aditya and Oliva, Aude and Torralba, Antonio}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/cvpr/BauZKO017.bib}, - booktitle = {2017 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2017, Honolulu, HI, USA, July 21-26, 2017}, - doi = {10.1109/CVPR.2017.354}, + booktitle = {2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + doi = {10.1109/cvpr.2017.354}, pages = {3319--3327}, - publisher = {{IEEE} Computer Society}, + publisher = {IEEE}, timestamp = {Fri, 25 Dec 2020 00:00:00 +0100}, - title = {Network Dissection: Quantifying Interpretability of Deep Visual Representations}, - url = {https://doi.org/10.1109/CVPR.2017.354}, - year = {2017} + title = {Network Dissection: {Quantifying} Interpretability of Deep Visual Representations}, + url = {https://doi.org/10.1109/cvpr.2017.354}, + year = {2017}, + source = {Crossref}, + month = jul, } @article{beck1998beyond, @@ -81,7 +88,8 @@ @article{beck1998beyond title = {Beyond Linearity by Default: {Generalized} Additive Models}, url = {https://doi.org/10.2307/2991772}, volume = {42}, - year = {1998} + year = {1998}, + month = apr, } @inproceedings{bhagoji2018practical, @@ -89,7 +97,7 @@ @inproceedings{bhagoji2018practical booktitle = {Proceedings of the European conference on computer vision (ECCV)}, pages = {154--169}, title = {Practical black-box attacks on deep neural networks using efficient query mechanisms}, - year = {2018} + year = {2018}, } @inproceedings{bourtoule2021machine, @@ -102,7 +110,8 @@ @inproceedings{bourtoule2021machine source = {Crossref}, title = {Machine Unlearning}, url = {https://doi.org/10.1109/sp40001.2021.00019}, - year = {2021} + year = {2021}, + month = may, } @article{bricken2023towards, @@ -115,7 +124,7 @@ @article{bricken2023towards title = {Closing the Wearable Gap: {Foot{\textendash}ankle} kinematic modeling via deep learning models based on a smart sock wearable}, url = {https://doi.org/10.1017/wtc.2023.3}, volume = {4}, - year = {2023} + year = {2023}, } @inproceedings{buolamwini2018genderShades, @@ -124,7 +133,7 @@ @inproceedings{buolamwini2018genderShades organization = {PMLR}, pages = {77--91}, title = {Gender shades: {Intersectional} accuracy disparities in commercial gender classification}, - year = {2018} + year = {2018}, } @article{calvo2020supporting, @@ -133,7 +142,7 @@ @article{calvo2020supporting pages = {31--54}, publisher = {Springer}, title = {Supporting human autonomy in {AI} systems: {A} framework for ethical enquiry}, - year = {2020} + year = {2020}, } @inproceedings{carlini2016hidden, @@ -141,7 +150,7 @@ @inproceedings{carlini2016hidden booktitle = {25th USENIX security symposium (USENIX security 16)}, pages = {513--530}, title = {Hidden voice commands}, - year = {2016} + year = {2016}, } @inproceedings{carlini2023extracting, @@ -153,40 +162,40 @@ @inproceedings{carlini2023extracting source = {Crossref}, title = {Preventing Generation of Verbatim Memorization in Language Models Gives a False Sense of Privacy}, url = {https://doi.org/10.18653/v1/2023.inlg-main.3}, - year = {2023} + year = {2023}, } @inproceedings{chen2019looks, - author = {Chaofan Chen and Oscar Li and Daniel Tao and Alina Barnett and Cynthia Rudin and Jonathan Su}, + author = {Chen, Chaofan and Li, Oscar and Tao, Daniel and Barnett, Alina and Rudin, Cynthia and Su, Jonathan}, + editor = {Wallach, Hanna M. and Larochelle, Hugo and Beygelzimer, Alina and d'Alch\'e-Buc, Florence and Fox, Emily B. and Garnett, Roman}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/ChenLTBRS19.bib}, booktitle = {Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada}, - editor = {Hanna M. Wallach and Hugo Larochelle and Alina Beygelzimer and Florence d'Alch{\'{e}}{-}Buc and Emily B. Fox and Roman Garnett}, pages = {8928--8939}, timestamp = {Thu, 21 Jan 2021 00:00:00 +0100}, - title = {This Looks Like That: Deep Learning for Interpretable Image Recognition}, + title = {This Looks Like That: {Deep} Learning for Interpretable Image Recognition}, url = {https://proceedings.neurips.cc/paper/2019/hash/adf7ee2dcf142b0e11888e72b43fcb75-Abstract.html}, - year = {2019} + year = {2019}, } @inproceedings{christiano2017deep, - author = {Paul F. Christiano and Jan Leike and Tom B. Brown and Miljan Martic and Shane Legg and Dario Amodei}, + author = {Christiano, Paul F. and Leike, Jan and Brown, Tom B. and Martic, Miljan and Legg, Shane and Amodei, Dario}, + editor = {Guyon, Isabelle and von Luxburg, Ulrike and Bengio, Samy and Wallach, Hanna M. and Fergus, Rob and Vishwanathan, S. V. N. and Garnett, Roman}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/ChristianoLBMLA17.bib}, - booktitle = {Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, {USA}}, - editor = {Isabelle Guyon and Ulrike von Luxburg and Samy Bengio and Hanna M. Wallach and Rob Fergus and S. V. N. Vishwanathan and Roman Garnett}, + booktitle = {Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA}, pages = {4299--4307}, timestamp = {Thu, 21 Jan 2021 00:00:00 +0100}, title = {Deep Reinforcement Learning from Human Preferences}, url = {https://proceedings.neurips.cc/paper/2017/hash/d5e2c0adad503c91f91df240d0cd4e49-Abstract.html}, - year = {2017} + year = {2017}, } @book{d2023dataFeminism, author = {D'ignazio, Catherine and Klein, Lauren F}, publisher = {MIT press}, title = {Data feminism}, - year = {2023} + year = {2023}, } @article{friedman1996value, @@ -201,20 +210,21 @@ @article{friedman1996value title = {Value-sensitive design}, url = {https://doi.org/10.1145/242485.242493}, volume = {3}, - year = {1996} + year = {1996}, + month = dec, } @inproceedings{geiger2021causal, - author = {Atticus Geiger and Hanson Lu and Thomas Icard and Christopher Potts}, + author = {Geiger, Atticus and Lu, Hanson and Icard, Thomas and Potts, Christopher}, + editor = {Ranzato, Marc'Aurelio and Beygelzimer, Alina and Dauphin, Yann N. and Liang, Percy and Vaughan, Jennifer Wortman}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/GeigerLIP21.bib}, booktitle = {Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual}, - editor = {Marc'Aurelio Ranzato and Alina Beygelzimer and Yann N. Dauphin and Percy Liang and Jennifer Wortman Vaughan}, pages = {9574--9586}, timestamp = {Tue, 03 May 2022 01:00:00 +0200}, title = {Causal Abstractions of Neural Networks}, url = {https://proceedings.neurips.cc/paper/2021/hash/4f5c422f4d49a5a807eda27434231040-Abstract.html}, - year = {2021} + year = {2021}, } @article{gupta2016monotonic, @@ -225,7 +235,7 @@ @article{gupta2016monotonic publisher = {JMLR. org}, title = {Monotonic calibrated interpolated look-up tables}, volume = {17}, - year = {2016} + year = {2016}, } @article{handlin1965science, @@ -234,36 +244,36 @@ @article{handlin1965science pages = {156--170}, publisher = {JSTOR}, title = {Science and technology in popular culture}, - year = {1965} + year = {1965}, } @inproceedings{hardt2016equality, - author = {Moritz Hardt and Eric Price and Nati Srebro}, + author = {Hardt, Moritz and Price, Eric and Srebro, Nati}, + editor = {Lee, Daniel D. and Sugiyama, Masashi and von Luxburg, Ulrike and Guyon, Isabelle and Garnett, Roman}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/HardtPNS16.bib}, booktitle = {Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain}, - editor = {Daniel D. Lee and Masashi Sugiyama and Ulrike von Luxburg and Isabelle Guyon and Roman Garnett}, pages = {3315--3323}, timestamp = {Thu, 21 Jan 2021 00:00:00 +0100}, title = {Equality of Opportunity in Supervised Learning}, url = {https://proceedings.neurips.cc/paper/2016/hash/9d2682367c3935defcb1f9e247a97c0d-Abstract.html}, - year = {2016} + year = {2016}, } @inproceedings{hebert2018multicalibration, - author = {{\'{U}}rsula H{\'{e}}bert{-}Johnson and Michael P. Kim and Omer Reingold and Guy N. Rothblum}, + author = {H\'ebert-Johnson, \'Ursula and Kim, Michael P. and Reingold, Omer and Rothblum, Guy N.}, + editor = {Dy, Jennifer G. and Krause, Andreas}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/icml/Hebert-JohnsonK18.bib}, - booktitle = {Proceedings of the 35th International Conference on Machine Learning, {ICML} 2018, Stockholmsm{\"{a}}ssan, Stockholm, Sweden, July 10-15, 2018}, - editor = {Jennifer G. Dy and Andreas Krause}, + booktitle = {Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm\"assan, Stockholm, Sweden, July 10-15, 2018}, pages = {1944--1953}, - publisher = {{PMLR}}, + publisher = {PMLR}, series = {Proceedings of Machine Learning Research}, timestamp = {Wed, 03 Apr 2019 01:00:00 +0200}, - title = {Multicalibration: Calibration for the (Computationally-Identifiable) Masses}, + title = {Multicalibration: {Calibration} for the {(Computationally}-Identifiable) Masses}, url = {http://proceedings.mlr.press/v80/hebert-johnson18a.html}, volume = {80}, - year = {2018} + year = {2018}, } @article{himmelstein2022examination, @@ -278,53 +288,59 @@ @article{himmelstein2022examination title = {Examination of Stigmatizing Language in the Electronic Health Record}, url = {https://doi.org/10.1001/jamanetworkopen.2021.44967}, volume = {5}, - year = {2022} + year = {2022}, + month = jan, } @inproceedings{kaur2020interpreting, - author = {Harmanpreet Kaur and Harsha Nori and Samuel Jenkins and Rich Caruana and Hanna M. Wallach and Jennifer Wortman Vaughan}, + author = {Kaur, Harmanpreet and Nori, Harsha and Jenkins, Samuel and Caruana, Rich and Wallach, Hanna and Wortman Vaughan, Jennifer}, + editor = {Bernhaupt, Regina and Mueller, Florian 'Floyd' and Verweij, David and Andres, Josh and McGrenere, Joanna and Cockburn, Andy and Avellino, Ignacio and Goguey, Alix and Bj{\o}n, Pernille and Zhao, Shengdong and Samson, Briane Paul and Kocielnik, Rafal}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/chi/KaurNJCWV20.bib}, - booktitle = {{CHI} '20: {CHI} Conference on Human Factors in Computing Systems, Honolulu, HI, USA, April 25-30, 2020}, + booktitle = {Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems}, doi = {10.1145/3313831.3376219}, - editor = {Regina Bernhaupt and Florian 'Floyd' Mueller and David Verweij and Josh Andres and Joanna McGrenere and Andy Cockburn and Ignacio Avellino and Alix Goguey and Pernille Bj{\o}n and Shengdong Zhao and Briane Paul Samson and Rafal Kocielnik}, pages = {1--14}, - publisher = {{ACM}}, + publisher = {ACM}, timestamp = {Tue, 12 May 2020 01:00:00 +0200}, - title = {Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning}, + title = {Interpreting Interpretability: {Understanding} Data Scientists' Use of Interpretability Tools for Machine Learning}, url = {https://doi.org/10.1145/3313831.3376219}, - year = {2020} + year = {2020}, + source = {Crossref}, + month = apr, } @inproceedings{kim2018interpretability, - author = {Been Kim and Martin Wattenberg and Justin Gilmer and Carrie J. Cai and James Wexler and Fernanda B. Vi{\'{e}}gas and Rory Sayres}, + author = {Cai, Carrie J. and Reif, Emily and Hegde, Narayan and Hipp, Jason and Kim, Been and Smilkov, Daniel and Wattenberg, Martin and Viegas, Fernanda and Corrado, Greg S. and Stumpe, Martin C. and Terry, Michael}, + editor = {Dy, Jennifer G. and Krause, Andreas}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/icml/KimWGCWVS18.bib}, - booktitle = {Proceedings of the 35th International Conference on Machine Learning, {ICML} 2018, Stockholmsm{\"{a}}ssan, Stockholm, Sweden, July 10-15, 2018}, - editor = {Jennifer G. Dy and Andreas Krause}, + booktitle = {Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems}, pages = {2673--2682}, - publisher = {{PMLR}}, + publisher = {ACM}, series = {Proceedings of Machine Learning Research}, timestamp = {Wed, 03 Apr 2019 01:00:00 +0200}, - title = {Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors {(TCAV)}}, - url = {http://proceedings.mlr.press/v80/kim18d.html}, + title = {Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making}, + url = {https://doi.org/10.1145/3290605.3300234}, volume = {80}, - year = {2018} + year = {2019}, + doi = {10.1145/3290605.3300234}, + source = {Crossref}, + month = may, } @inproceedings{koh2020concept, - author = {Pang Wei Koh and Thao Nguyen and Yew Siang Tang and Stephen Mussmann and Emma Pierson and Been Kim and Percy Liang}, + author = {Koh, Pang Wei and Nguyen, Thao and Tang, Yew Siang and Mussmann, Stephen and Pierson, Emma and Kim, Been and Liang, Percy}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/icml/KohNTMPKL20.bib}, - booktitle = {Proceedings of the 37th International Conference on Machine Learning, {ICML} 2020, 13-18 July 2020, Virtual Event}, + booktitle = {Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event}, pages = {5338--5348}, - publisher = {{PMLR}}, + publisher = {PMLR}, series = {Proceedings of Machine Learning Research}, timestamp = {Tue, 15 Dec 2020 00:00:00 +0100}, title = {Concept Bottleneck Models}, url = {http://proceedings.mlr.press/v119/koh20a.html}, volume = {119}, - year = {2020} + year = {2020}, } @inproceedings{lakkaraju2020fool, @@ -337,48 +353,51 @@ @inproceedings{lakkaraju2020fool subtitle = {Manipulating User Trust via Misleading Black Box Explanations}, title = {{''How} do I fool you?''}, url = {https://doi.org/10.1145/3375627.3375833}, - year = {2020} + year = {2020}, + month = feb, } @book{lindgren2023handbook, author = {Lindgren, Simon}, publisher = {Edward Elgar Publishing}, title = {Handbook of Critical Studies of Artificial Intelligence}, - year = {2023} + year = {2023}, } @inproceedings{lou2013accurate, - author = {Yin Lou and Rich Caruana and Johannes Gehrke and Giles Hooker}, + author = {Lou, Yin and Caruana, Rich and Gehrke, Johannes and Hooker, Giles}, + editor = {Dhillon, Inderjit S. and Koren, Yehuda and Ghani, Rayid and Senator, Ted E. and Bradley, Paul and Parekh, Rajesh and He, Jingrui and Grossman, Robert L. and Uthurusamy, Ramasamy}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/kdd/LouCGH13.bib}, - booktitle = {The 19th {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining, {KDD} 2013, Chicago, IL, USA, August 11-14, 2013}, + booktitle = {Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining}, doi = {10.1145/2487575.2487579}, - editor = {Inderjit S. Dhillon and Yehuda Koren and Rayid Ghani and Ted E. Senator and Paul Bradley and Rajesh Parekh and Jingrui He and Robert L. Grossman and Ramasamy Uthurusamy}, pages = {623--631}, - publisher = {{ACM}}, + publisher = {ACM}, timestamp = {Fri, 25 Dec 2020 00:00:00 +0100}, title = {Accurate intelligible models with pairwise interactions}, url = {https://doi.org/10.1145/2487575.2487579}, - year = {2013} + year = {2013}, + source = {Crossref}, + month = aug, } @article{lowy2021fermi, author = {Lowy, Andrew and Pavan, Rakesh and Baharlouei, Sina and Razaviyayn, Meisam and Beirami, Ahmad}, title = {Fermi: {Fair} empirical risk minimization via exponential R\'enyi mutual information}, - year = {2021} + year = {2021}, } @inproceedings{lundberg2017unified, - author = {Scott M. Lundberg and Su{-}In Lee}, + author = {Lundberg, Scott M. and Lee, Su-In}, + editor = {Guyon, Isabelle and von Luxburg, Ulrike and Bengio, Samy and Wallach, Hanna M. and Fergus, Rob and Vishwanathan, S. V. N. and Garnett, Roman}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/LundbergL17.bib}, - booktitle = {Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, {USA}}, - editor = {Isabelle Guyon and Ulrike von Luxburg and Samy Bengio and Hanna M. Wallach and Rob Fergus and S. V. N. Vishwanathan and Roman Garnett}, + booktitle = {Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA}, pages = {4765--4774}, timestamp = {Thu, 21 Jan 2021 00:00:00 +0100}, title = {A Unified Approach to Interpreting Model Predictions}, url = {https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html}, - year = {2017} + year = {2017}, } @article{martin1993myth, @@ -393,7 +412,8 @@ @article{martin1993myth title = {The myth of the awesome thinking machine}, url = {https://doi.org/10.1145/255950.153587}, volume = {36}, - year = {1993} + year = {1993}, + month = apr, } @incollection{mccarthy1981epistemological, @@ -405,7 +425,8 @@ @incollection{mccarthy1981epistemological source = {Crossref}, title = {Epistemological Problems Of Artificial Intelligence}, url = {https://doi.org/10.1016/b978-0-934613-03-3.50035-0}, - year = {1981} + year = {1981}, + isbn = {9780934613033}, } @article{ng2021ai, @@ -416,7 +437,7 @@ @article{ng2021ai publisher = {Wiley Online Library}, title = {{AI} literacy: {Definition,} teaching, evaluation and ethical issues}, volume = {58}, - year = {2021} + year = {2021}, } @article{ngo2022alignment, @@ -425,7 +446,7 @@ @article{ngo2022alignment title = {The alignment problem from a deep learning perspective}, url = {https://arxiv.org/abs/2209.00626}, volume = {abs/2209.00626}, - year = {2022} + year = {2022}, } @article{obermeyer2019dissecting, @@ -440,7 +461,8 @@ @article{obermeyer2019dissecting title = {Dissecting racial bias in an algorithm used to manage the health of populations}, url = {https://doi.org/10.1126/science.aax2342}, volume = {366}, - year = {2019} + year = {2019}, + month = oct, } @article{olah2020zoom, @@ -455,7 +477,8 @@ @article{olah2020zoom title = {Zoom In: {An} Introduction to Circuits}, url = {https://doi.org/10.23915/distill.00024.001}, volume = {5}, - year = {2020} + year = {2020}, + month = mar, } @article{peters2018designing, @@ -469,7 +492,8 @@ @article{peters2018designing title = {Designing for Motivation, Engagement and Wellbeing in Digital Experience}, url = {https://doi.org/10.3389/fpsyg.2018.00797}, volume = {9}, - year = {2018} + year = {2018}, + month = may, } @inproceedings{ramaswamy2023overlooked, @@ -481,7 +505,8 @@ @inproceedings{ramaswamy2023overlooked source = {Crossref}, title = {Overlooked Factors in Concept-Based Explanations: {Dataset} Choice, Concept Learnability, and Human Capability}, url = {https://doi.org/10.1109/cvpr52729.2023.01052}, - year = {2023} + year = {2023}, + month = jun, } @article{ramaswamy2023ufo, @@ -490,7 +515,7 @@ @article{ramaswamy2023ufo title = {{UFO:} {A} unified method for controlling Understandability and Faithfulness Objectives in concept-based explanations for {CNNs}}, url = {https://arxiv.org/abs/2303.15632}, volume = {abs/2303.15632}, - year = {2023} + year = {2023}, } @inproceedings{ribeiro2016should, @@ -498,7 +523,7 @@ @inproceedings{ribeiro2016should booktitle = {Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining}, pages = {1135--1144}, title = {{\textquotedblright} Why should i trust you?{\textquotedblright} Explaining the predictions of any classifier}, - year = {2016} + year = {2016}, } @article{rudin2019stop, @@ -513,7 +538,8 @@ @article{rudin2019stop title = {Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead}, url = {https://doi.org/10.1038/s42256-019-0048-x}, volume = {1}, - year = {2019} + year = {2019}, + month = may, } @article{russell2021human, @@ -522,7 +548,7 @@ @article{russell2021human pages = {3--23}, publisher = {Oxford University Press Oxford}, title = {Human-compatible artificial intelligence}, - year = {2021} + year = {2021}, } @article{ryan2000self, @@ -537,7 +563,7 @@ @article{ryan2000self title = {Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being.}, url = {https://doi.org/10.1037/0003-066x.55.1.68}, volume = {55}, - year = {2000} + year = {2000}, } @article{schafer2023notorious, @@ -552,21 +578,41 @@ @article{schafer2023notorious title = {The Notorious {GPT:} {Science} communication in the age of artificial intelligence}, url = {https://doi.org/10.22323/2.22020402}, volume = {22}, - year = {2023} + year = {2023}, + month = may, +} + +@article{schneiderman2020, + author = {Shneiderman, Ben}, + year = {2020}, + month = oct, + pages = {1--31}, + title = {Bridging the Gap Between Ethics and Practice}, + volume = {10}, + journal = {ACM Trans. Interact. Intell. Syst.}, + doi = {10.1145/3419764}, + number = {4}, + source = {Crossref}, + url = {https://doi.org/10.1145/3419764}, + publisher = {Association for Computing Machinery (ACM)}, + subtitle = {Guidelines for Reliable, Safe, and Trustworthy Human-centered AI Systems}, + issn = {2160-6455, 2160-6463}, } @inproceedings{selvaraju2017grad, - author = {Ramprasaath R. Selvaraju and Michael Cogswell and Abhishek Das and Ramakrishna Vedantam and Devi Parikh and Dhruv Batra}, + author = {Selvaraju, Ramprasaath R. and Cogswell, Michael and Das, Abhishek and Vedantam, Ramakrishna and Parikh, Devi and Batra, Dhruv}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/iccv/SelvarajuCDVPB17.bib}, - booktitle = {{IEEE} International Conference on Computer Vision, {ICCV} 2017, Venice, Italy, October 22-29, 2017}, - doi = {10.1109/ICCV.2017.74}, + booktitle = {2017 IEEE International Conference on Computer Vision (ICCV)}, + doi = {10.1109/iccv.2017.74}, pages = {618--626}, - publisher = {{IEEE} Computer Society}, + publisher = {IEEE}, timestamp = {Fri, 25 Dec 2020 00:00:00 +0100}, - title = {Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization}, - url = {https://doi.org/10.1109/ICCV.2017.74}, - year = {2017} + title = {Grad-{CAM:} {Visual} Explanations from Deep Networks via Gradient-Based Localization}, + url = {https://doi.org/10.1109/iccv.2017.74}, + year = {2017}, + source = {Crossref}, + month = oct, } @article{shalev2017formal, @@ -575,14 +621,14 @@ @article{shalev2017formal title = {On a formal model of safe and scalable self-driving cars}, url = {https://arxiv.org/abs/1708.06374}, volume = {abs/1708.06374}, - year = {2017} + year = {2017}, } @book{shneiderman2022human, author = {Shneiderman, Ben}, publisher = {Oxford University Press}, title = {Human-centered {AI}}, - year = {2022} + year = {2022}, } @inproceedings{shokri2017membership, @@ -595,7 +641,8 @@ @inproceedings{shokri2017membership source = {Crossref}, title = {Membership Inference Attacks Against Machine Learning Models}, url = {https://doi.org/10.1109/sp.2017.41}, - year = {2017} + year = {2017}, + month = may, } @article{smilkov2017smoothgrad, @@ -604,19 +651,19 @@ @article{smilkov2017smoothgrad title = {Smoothgrad: {Removing} noise by adding noise}, url = {https://arxiv.org/abs/1706.03825}, volume = {abs/1706.03825}, - year = {2017} + year = {2017}, } @inproceedings{szegedy2013intriguing, - author = {Christian Szegedy and Wojciech Zaremba and Ilya Sutskever and Joan Bruna and Dumitru Erhan and Ian J. Goodfellow and Rob Fergus}, + author = {Szegedy, Christian and Zaremba, Wojciech and Sutskever, Ilya and Bruna, Joan and Erhan, Dumitru and Goodfellow, Ian J. and Fergus, Rob}, + editor = {Bengio, Yoshua and LeCun, Yann}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/journals/corr/SzegedyZSBEGF13.bib}, - booktitle = {2nd International Conference on Learning Representations, {ICLR} 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings}, - editor = {Yoshua Bengio and Yann LeCun}, + booktitle = {2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings}, timestamp = {Thu, 25 Jul 2019 01:00:00 +0200}, title = {Intriguing properties of neural networks}, url = {http://arxiv.org/abs/1312.6199}, - year = {2014} + year = {2014}, } @inproceedings{tramer2019adversarial, @@ -629,7 +676,8 @@ @inproceedings{tramer2019adversarial subtitle = {Perceptual Ad Blocking meets Adversarial Machine Learning}, title = {{AdVersarial}}, url = {https://doi.org/10.1145/3319535.3354222}, - year = {2019} + year = {2019}, + month = nov, } @article{wachter2017counterfactual, @@ -643,7 +691,7 @@ @article{wachter2017counterfactual title = {Counterfactual Explanations Without Opening the Black Box: {Automated} Decisions and the {GDPR}}, url = {https://doi.org/10.2139/ssrn.3063289}, volume = {31}, - year = {2017} + year = {2017}, } @article{wang2022interpretability, @@ -658,7 +706,8 @@ @article{wang2022interpretability title = {A conceptual peer review model for {arXiv} and other preprint databases}, url = {https://doi.org/10.1002/leap.1229}, volume = {32}, - year = {2019} + year = {2019}, + month = feb, } @article{wiener1960some, @@ -674,7 +723,8 @@ @article{wiener1960some title = {Some Moral and Technical Consequences of Automation}, url = {https://doi.org/10.1126/science.131.3410.1355}, volume = {131}, - year = {1960} + year = {1960}, + month = may, } @inproceedings{zhou2018interpretable, @@ -682,16 +732,5 @@ @inproceedings{zhou2018interpretable booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)}, pages = {119--134}, title = {Interpretable basis decomposition for visual explanation}, - year = {2018} + year = {2018}, } - -@article{schneiderman2020, -author = {Shneiderman, Ben}, -year = {2020}, -month = {12}, -pages = {1-31}, -title = {Bridging the Gap Between Ethics and Practice: Guidelines for Reliable, Safe, and Trustworthy Human-centered AI Systems}, -volume = {10}, -journal = {ACM Transactions on Interactive Intelligent Systems}, -doi = {10.1145/3419764} -} \ No newline at end of file diff --git a/contents/sustainable_ai/sustainable_ai.bib b/contents/sustainable_ai/sustainable_ai.bib index ab782546..86145885 100644 --- a/contents/sustainable_ai/sustainable_ai.bib +++ b/contents/sustainable_ai/sustainable_ai.bib @@ -1,9 +1,12 @@ +%comment{This file was created with betterbib v5.0.11.} + + @misc{anthony2020carbontracker, - author = {Lasse F. Wolff Anthony and Benjamin Kanding and Raghavendra Selvan}, + author = {Anthony, Lasse F. Wolff and Kanding, Benjamin and Selvan, Raghavendra}, howpublished = {ICML Workshop on Challenges in Deploying and monitoring Machine Learning Systems}, - month = {July}, + month = jul, note = {arXiv:2007.03051}, - year = {2020} + year = {2020}, } @book{barroso2019datacenter, @@ -16,7 +19,7 @@ @book{barroso2019datacenter subtitle = {Designing Warehouse-Scale Machines}, title = {The Datacenter as a Computer}, url = {https://doi.org/10.1007/978-3-031-01761-2}, - year = {2019} + year = {2019}, } @incollection{bohr2020rise, @@ -29,36 +32,38 @@ @incollection{bohr2020rise source = {Crossref}, title = {The rise of artificial intelligence in healthcare applications}, url = {https://doi.org/10.1016/b978-0-12-818438-7.00002-2}, - year = {2020} + year = {2020}, } @inproceedings{bondi2018spot, - author = {Elizabeth Bondi and Ashish Kapoor and Debadeepta Dey and James Piavis and Shital Shah and Robert Hannaford and Arvind Iyer and Lucas Joppa and Milind Tambe}, + author = {Bondi, Elizabeth and Kapoor, Ashish and Dey, Debadeepta and Piavis, James and Shah, Shital and Hannaford, Robert and Iyer, Arvind and Joppa, Lucas and Tambe, Milind}, + editor = {Lang, J\'er\^ome}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/ijcai/BondiKDPSHIJT18.bib}, - booktitle = {Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, {IJCAI} 2018, July 13-19, 2018, Stockholm, Sweden}, + booktitle = {Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence}, doi = {10.24963/ijcai.2018/847}, - editor = {J{\'{e}}r{\^{o}}me Lang}, pages = {5814--5816}, - publisher = {ijcai.org}, + publisher = {International Joint Conferences on Artificial Intelligence Organization}, timestamp = {Tue, 20 Aug 2019 01:00:00 +0200}, - title = {Near Real-Time Detection of Poachers from Drones in AirSim}, + title = {Near Real-Time Detection of Poachers from Drones in {AirSim}}, url = {https://doi.org/10.24963/ijcai.2018/847}, - year = {2018} + year = {2018}, + source = {Crossref}, + month = jul, } @misc{buyya2010energyefficient, - archiveprefix = {arXiv}, author = {Buyya, Rajkumar and Beloglazov, Anton and Abawajy, Jemal}, + archiveprefix = {arXiv}, eprint = {1006.0308}, primaryclass = {cs.DC}, title = {Energy-Efficient Management of Data Center Resources for Cloud Computing: {A} Vision, Architectural Elements, and Open Challenges}, - year = {2010} + year = {2010}, } @article{cenci2021ecofriendly, - abstract = {Abstract Eco-friendliness is becoming an indispensable feature for electrical and electronic equipment to thrive in the competitive market. This comprehensive review is the first to define eco-friendly electronics in its multiple meanings: power saving devices, end-of-life impact attenuators, equipment whose manufacturing uses green processing, electronics that use materials that minimize environmental and health risks, designs that improve lifespan, reparability, etc. More specifically, this review discusses eco-friendly technologies and materials that are being introduced to replace the well-established ones. This is done for all material classes (metals, polymers, ceramics, and composites). Manufacturing, recycling, and final product characteristics are discussed in their various interconnected aspects. Additionally, the concept of consciously planned obsolescence is introduced to address the paradoxical relationship between durability and efficiency. The overall conclusions are that there is an important global trend to make electronics more eco-friendly. However, matching the performance and stability of well-established materials and technologies seems to be the main barrier to achieve it. These new implementations can have detrimental or beneficial net impacts on the environment. Assessing their net outcome is challenging because their impacts are frequently unknown and the current evaluation methods (and tools) are incapable of comprehensively quantifying these impacts and generating reliable verdicts.}, author = {Cenci, Marcelo Pilotto and Scarazzato, Tatiana and Munchen, Daniel Dotto and Dartora, Paula Cristina and Veit, Hugo Marcelo and Bernardes, Andrea Moura and Dias, Pablo R.}, + abstract = {Abstract Eco-friendliness is becoming an indispensable feature for electrical and electronic equipment to thrive in the competitive market. This comprehensive review is the first to define eco-friendly electronics in its multiple meanings: power saving devices, end-of-life impact attenuators, equipment whose manufacturing uses green processing, electronics that use materials that minimize environmental and health risks, designs that improve lifespan, reparability, etc. More specifically, this review discusses eco-friendly technologies and materials that are being introduced to replace the well-established ones. This is done for all material classes (metals, polymers, ceramics, and composites). Manufacturing, recycling, and final product characteristics are discussed in their various interconnected aspects. Additionally, the concept of consciously planned obsolescence is introduced to address the paradoxical relationship between durability and efficiency. The overall conclusions are that there is an important global trend to make electronics more eco-friendly. However, matching the performance and stability of well-established materials and technologies seems to be the main barrier to achieve it. These new implementations can have detrimental or beneficial net impacts on the environment. Assessing their net outcome is challenging because their impacts are frequently unknown and the current evaluation methods (and tools) are incapable of comprehensively quantifying these impacts and generating reliable verdicts.}, doi = {10.1002/admt.202001263}, issn = {2365-709X, 2365-709X}, journal = {Adv. Mater. Technol.}, @@ -71,14 +76,15 @@ @article{cenci2021ecofriendly title = {{Eco-Friendly} {Electronics{\textemdash}A} Comprehensive Review}, url = {https://doi.org/10.1002/admt.202001263}, volume = {7}, - year = {2021} + year = {2021}, + month = apr, } @inproceedings{challenge2021supply, author = {Challenge, WEF Net-Zero}, booktitle = {World Economic Forum: Geneva, Switzerland}, title = {The Supply Chain Opportunity}, - year = {2021} + year = {2021}, } @article{chen2006gallium, @@ -93,7 +99,8 @@ @article{chen2006gallium title = {Gallium, Indium, and Arsenic Pollution of Groundwater from a Semiconductor Manufacturing Area of {Taiwan}}, url = {https://doi.org/10.1007/s00128-006-1062-3}, volume = {77}, - year = {2006} + year = {2006}, + month = aug, } @article{chua1971memristor, @@ -108,7 +115,7 @@ @article{chua1971memristor title = {Memristor-The missing circuit element}, url = {https://doi.org/10.1109/tct.1971.1083337}, volume = {18}, - year = {1971} + year = {1971}, } @inproceedings{cooper2011semiconductor, @@ -121,7 +128,8 @@ @inproceedings{cooper2011semiconductor source = {Crossref}, title = {A semiconductor company's examination of its water footprint approach}, url = {https://doi.org/10.1109/issst.2011.5936865}, - year = {2011} + year = {2011}, + month = may, } @article{cope2009pure, @@ -130,7 +138,7 @@ @article{cope2009pure number = {10}, title = {Pure water, semiconductors and the recession}, volume = {10}, - year = {2009} + year = {2009}, } @techreport{davies2011endangered, @@ -138,14 +146,14 @@ @techreport{davies2011endangered pages = {50--54}, title = {Endangered elements: {Critical} thinking}, url = {https://www.rsc.org/images/Endangered\%20Elements\%20-\%20Critical\%20Thinking\_tcm18-196054.pdf}, - year = {2011} + year = {2011}, } @techreport{davis2022uptime, author = {Davis, Jacqueline and Bizo, Daniel and Lawrence, Andy and Rogers, Owen and Smolaks, Max}, institution = {Uptime Institute}, title = {Uptime Institute Global Data Center Survey 2022}, - year = {2022} + year = {2022}, } @article{dayarathna2015data, @@ -160,12 +168,12 @@ @article{dayarathna2015data title = {Data Center Energy Consumption Modeling: {A} Survey}, url = {https://doi.org/10.1109/comst.2015.2481183}, volume = {18}, - year = {2016} + year = {2016}, } @article{ebrahimi2014review, - abstract = {The depletion of the world's limited reservoirs of fossil fuels, the worldwide impact of global warming and the high cost of energy are among the primary issues driving a renewed interest in the capture and reuse of waste energy. A major source of waste energy is being created by data centers through the increasing demand for cloud based connectivity and performance. In fact, recent figures show that data centers are responsible for more than 2\% of the US total electricity usage. Almost half of this power is used for cooling the electronics, creating a significant stream of waste heat. The difficulty associated with recovering and reusing this stream of waste heat is that the heat is of low quality. In this paper, the most promising methods and technologies for recovering data center low-grade waste heat in an effective and economically reasonable way are identified and discussed. A number of currently available and developmental low-grade waste heat recovery techniques including district/plant/water heating, absorption cooling, direct power generation (piezoelectric and thermoelectric), indirect power generation (steam and organic Rankine cycle), biomass co-location, and desalination/clean water are reviewed along with their operational requirements in order to assess the suitability and effectiveness of each technology for data center applications. Based on a comparison between data centers' operational thermodynamic conditions and the operational requirements of the discussed waste heat recovery techniques, absorption cooling and organic Rankine cycle are found to be among the most promising technologies for data center waste heat reuse.}, author = {Ebrahimi, Khosrow and Jones, Gerard F. and Fleischer, Amy S.}, + abstract = {The depletion of the world's limited reservoirs of fossil fuels, the worldwide impact of global warming and the high cost of energy are among the primary issues driving a renewed interest in the capture and reuse of waste energy. A major source of waste energy is being created by data centers through the increasing demand for cloud based connectivity and performance. In fact, recent figures show that data centers are responsible for more than 2\% of the US total electricity usage. Almost half of this power is used for cooling the electronics, creating a significant stream of waste heat. The difficulty associated with recovering and reusing this stream of waste heat is that the heat is of low quality. In this paper, the most promising methods and technologies for recovering data center low-grade waste heat in an effective and economically reasonable way are identified and discussed. A number of currently available and developmental low-grade waste heat recovery techniques including district/plant/water heating, absorption cooling, direct power generation (piezoelectric and thermoelectric), indirect power generation (steam and organic Rankine cycle), biomass co-location, and desalination/clean water are reviewed along with their operational requirements in order to assess the suitability and effectiveness of each technology for data center applications. Based on a comparison between data centers' operational thermodynamic conditions and the operational requirements of the discussed waste heat recovery techniques, absorption cooling and organic Rankine cycle are found to be among the most promising technologies for data center waste heat reuse.}, doi = {10.1016/j.rser.2013.12.007}, issn = {1364-0321}, journal = {Renewable Sustainable Energy Rev.}, @@ -176,14 +184,15 @@ @article{ebrahimi2014review title = {A review of data center cooling technology, operating conditions and the corresponding low-grade waste heat recovery opportunities}, url = {https://doi.org/10.1016/j.rser.2013.12.007}, volume = {31}, - year = {2014} + year = {2014}, + month = mar, } @book{grossman2007high, author = {Grossman, Elizabeth}, publisher = {Island press}, title = {High tech trash: {Digital} devices, hidden toxics, and human health}, - year = {2007} + year = {2007}, } @inproceedings{gupta2022, @@ -196,7 +205,8 @@ @inproceedings{gupta2022 subtitle = {designing sustainable computer systems with an architectural carbon modeling tool}, title = {Act}, url = {https://doi.org/10.1145/3470496.3527408}, - year = {2022} + year = {2022}, + month = jun, } @article{henderson2020towards, @@ -207,7 +217,7 @@ @article{henderson2020towards publisher = {JMLRORG}, title = {Towards the systematic reporting of the energy and carbon footprints of machine learning}, volume = {21}, - year = {2020} + year = {2020}, } @article{hsu2016accumulation, @@ -222,12 +232,13 @@ @article{hsu2016accumulation title = {Accumulation of heavy metals and trace elements in fluvial sediments received effluents from traditional and semiconductor industries}, url = {https://doi.org/10.1038/srep34250}, volume = {6}, - year = {2016} + year = {2016}, + month = sep, } @article{irimiavladu2014textquotedblleftgreentextquotedblright, - abstract = {{\textquotedblleft}Green{\textquotedblright} electronics represents not only a novel scientific term but also an emerging area of research aimed at identifying compounds of natural origin and establishing economically efficient routes for the production of synthetic materials that have applicability in environmentally safe (biodegradable) and/or biocompatible devices. The ultimate goal of this research is to create paths for the production of human- and environmentally friendly electronics in general and the integration of such electronic circuits with living tissue in particular. Researching into the emerging class of {\textquotedblleft}green{\textquotedblright} electronics may help fulfill not only the original promise of organic electronics that is to deliver low-cost and energy efficient materials and devices but also achieve unimaginable functionalities for electronics, for example benign integration into life and environment. This Review will highlight recent research advancements in this emerging group of materials and their integration in unconventional organic electronic devices.}, author = {Irimia-Vladu, Mihai}, + abstract = {{\textquotedblleft}Green{\textquotedblright} electronics represents not only a novel scientific term but also an emerging area of research aimed at identifying compounds of natural origin and establishing economically efficient routes for the production of synthetic materials that have applicability in environmentally safe (biodegradable) and/or biocompatible devices. The ultimate goal of this research is to create paths for the production of human- and environmentally friendly electronics in general and the integration of such electronic circuits with living tissue in particular. Researching into the emerging class of {\textquotedblleft}green{\textquotedblright} electronics may help fulfill not only the original promise of organic electronics that is to deliver low-cost and energy efficient materials and devices but also achieve unimaginable functionalities for electronics, for example benign integration into life and environment. This Review will highlight recent research advancements in this emerging group of materials and their integration in unconventional organic electronic devices.}, doi = {10.1039/c3cs60235d}, issn = {0306-0012, 1460-4744}, journal = {Chem. Soc. Rev.}, @@ -239,16 +250,16 @@ @article{irimiavladu2014textquotedblleftgreentextquotedblright title = {{{\textquotedblleft}Green{\textquotedblright}} electronics: {Biodegradable} and biocompatible materials and devices for sustainable future}, url = {https://doi.org/10.1039/c3cs60235d}, volume = {43}, - year = {2014} + year = {2014}, } @article{jaewon2023perseus, - author = {Jae-Won Chung and Yile Gu and Insu Jang and Luoxi Meng and Nikhil Bansal and Mosharaf Chowdhury}, + author = {Chung, Jae-Won and Gu, Yile and Jang, Insu and Meng, Luoxi and Bansal, Nikhil and Chowdhury, Mosharaf}, journal = {ArXiv preprint}, - title = {Perseus: Removing Energy Bloat from Large Model Training}, + title = {Perseus: {Removing} Energy Bloat from Large Model Training}, url = {https://arxiv.org/abs/2312.06902}, volume = {abs/2312.06902}, - year = {2023} + year = {2023}, } @book{jha2014rare, @@ -260,20 +271,21 @@ @book{jha2014rare subtitle = {Properties and Applications}, title = {Rare Earth Materials}, url = {https://doi.org/10.1201/b17045}, - year = {2014} + year = {2014}, + month = jun, } @inproceedings{jie2023zeus, + author = {You, Jie and Chung, Jae-Won and Chowdhury, Mosharaf}, address = {Boston, MA}, - author = {Jie You and Jae-Won Chung and Mosharaf Chowdhury}, booktitle = {20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)}, isbn = {978-1-939133-33-5}, - month = {April}, + month = apr, pages = {119--139}, publisher = {USENIX Association}, - title = {Zeus: Understanding and Optimizing {GPU} Energy Consumption of {DNN} Training}, + title = {Zeus: {Understanding} and Optimizing {GPU} Energy Consumption of {DNN} Training}, url = {https://www.usenix.org/conference/nsdi23/presentation/you}, - year = {2023} + year = {2023}, } @article{kaplan2020scaling, @@ -282,7 +294,7 @@ @article{kaplan2020scaling title = {Scaling Laws for Neural Language Models}, url = {https://arxiv.org/abs/2001.08361}, volume = {abs/2001.08361}, - year = {2020} + year = {2020}, } @article{kim2018chemical, @@ -297,7 +309,8 @@ @article{kim2018chemical title = {Chemical use in the semiconductor manufacturing industry}, url = {https://doi.org/10.1080/10773525.2018.1519957}, volume = {24}, - year = {2018} + year = {2018}, + month = oct, } @inproceedings{kurth2023fourcastnet, @@ -309,7 +322,8 @@ @inproceedings{kurth2023fourcastnet source = {Crossref}, title = {{FourCastNet:} {Accelerating} Global High-Resolution Weather Forecasting Using Adaptive {Fourier} Neural Operators}, url = {https://doi.org/10.1145/3592979.3593412}, - year = {2023} + year = {2023}, + month = jun, } @article{lam2023learning, @@ -317,23 +331,31 @@ @article{lam2023learning doi = {10.1126/science.adi2336}, issn = {0036-8075, 1095-9203}, journal = {Science}, - pages = {eadi2336}, + pages = {1416--1421}, publisher = {American Association for the Advancement of Science (AAAS)}, source = {Crossref}, title = {Learning skillful medium-range global weather forecasting}, url = {https://doi.org/10.1126/science.adi2336}, - year = {2023} + year = {2023}, + number = {6677}, + volume = {382}, + month = dec, } @article{lannelongue2021green, author = {Lannelongue, Lo{\"\i}c and Grealey, Jason and Inouye, Michael}, - journal = {Advanced science}, + journal = {Adv. Sci.}, number = {12}, pages = {2100707}, - publisher = {Wiley Online Library}, - title = {Green algorithms: quantifying the carbon footprint of computation}, + publisher = {Wiley}, + title = {Green Algorithms: {Quantifying} the Carbon Footprint of Computation}, volume = {8}, - year = {2021} + year = {2021}, + doi = {10.1002/advs.202100707}, + source = {Crossref}, + url = {https://doi.org/10.1002/advs.202100707}, + issn = {2198-3844, 2198-3844}, + month = may, } @article{lecocq2022mitigation, @@ -347,7 +369,8 @@ @article{lecocq2022mitigation title = {Examples of shifting development pathways: {Lessons} on how to enable broader, deeper, and faster climate action}, url = {https://doi.org/10.1007/s44168-022-00026-1}, volume = {1}, - year = {2022} + year = {2022}, + month = dec, } @article{liu2020energy, @@ -362,7 +385,8 @@ @article{liu2020energy title = {Energy consumption and emission mitigation prediction based on data center traffic and {PUE} for global data centers}, url = {https://doi.org/10.1016/j.gloei.2020.07.008}, volume = {3}, - year = {2020} + year = {2020}, + month = jun, } @article{maslej2023artificial, @@ -371,12 +395,12 @@ @article{maslej2023artificial title = {Artificial intelligence index report 2023}, url = {https://arxiv.org/abs/2310.03715}, volume = {abs/2310.03715}, - year = {2023} + year = {2023}, } @article{maxime2016impact, - abstract = {This paper studies government subsidies for green technology adoption while considering the manufacturing industry's response. Government subsidies offered directly to consumers impact the supplier's production and pricing decisions. Our analysis expands the current understanding of the price-setting newsvendor model, incorporating the external influence from the government, who is now an additional player in the system. We quantify how demand uncertainty impacts the various players (government, industry, and consumers) when designing policies. We further show that, for convex demand functions, an increase in demand uncertainty leads to higher production quantities and lower prices, resulting in lower profits for the supplier. With this in mind, one could expect consumer surplus to increase with uncertainty. In fact, we show that this is not always the case and that the uncertainty impact on consumer surplus depends on the trade-off between lower prices and the possibility of underserving customers with high valuations. We also show that when policy makers such as governments ignore demand uncertainty when designing consumer subsidies, they can significantly miss the desired adoption target level. From a coordination perspective, we demonstrate that the decentralized decisions are also optimal for a central planner managing jointly the supplier and the government. As a result, subsidies provide a coordination mechanism.}, author = {Cohen, Maxime C. and Lobel, Ruben and Perakis, Georgia}, + abstract = {This paper studies government subsidies for green technology adoption while considering the manufacturing industry's response. Government subsidies offered directly to consumers impact the supplier's production and pricing decisions. Our analysis expands the current understanding of the price-setting newsvendor model, incorporating the external influence from the government, who is now an additional player in the system. We quantify how demand uncertainty impacts the various players (government, industry, and consumers) when designing policies. We further show that, for convex demand functions, an increase in demand uncertainty leads to higher production quantities and lower prices, resulting in lower profits for the supplier. With this in mind, one could expect consumer surplus to increase with uncertainty. In fact, we show that this is not always the case and that the uncertainty impact on consumer surplus depends on the trade-off between lower prices and the possibility of underserving customers with high valuations. We also show that when policy makers such as governments ignore demand uncertainty when designing consumer subsidies, they can significantly miss the desired adoption target level. From a coordination perspective, we demonstrate that the decentralized decisions are also optimal for a central planner managing jointly the supplier and the government. As a result, subsidies provide a coordination mechanism.}, doi = {10.1287/mnsc.2015.2173}, issn = {0025-1909, 1526-5501}, journal = {Manage. Sci.}, @@ -388,7 +412,8 @@ @article{maxime2016impact url = {https://doi.org/10.1287/mnsc.2015.2173}, urldate = {2023-12-01}, volume = {62}, - year = {2016} + year = {2016}, + month = may, } @article{mills1997overview, @@ -403,7 +428,8 @@ @article{mills1997overview title = {An overview of semiconductor photocatalysis}, url = {https://doi.org/10.1016/s1010-6030(97)00118-4}, volume = {108}, - year = {1997} + year = {1997}, + month = jul, } @article{monyei2018electrons, @@ -417,29 +443,28 @@ @article{monyei2018electrons title = {Electrons have no identity: {Setting} right misrepresentations in Google and Apple{\textquoteright}s clean energy purchasing}, url = {https://doi.org/10.1016/j.erss.2018.06.015}, volume = {46}, - year = {2018} + year = {2018}, + month = dec, } @book{nakano2021geopolitics, author = {Nakano, Jane}, publisher = {JSTOR}, title = {The geopolitics of critical minerals supply chains}, - year = {2021} + year = {2021}, } -@article{patterson2022carbon, - author = {Patterson, David and Gonzalez, Joseph and Holzle, Urs and Le, Quoc and Liang, Chen and Munguia, Lluis-Miquel and Rothchild, Daniel and So, David R. and Texier, Maud and Dean, Jeff}, - doi = {10.1109/mc.2022.3148714}, - issn = {0018-9162, 1558-0814}, - journal = {Computer}, - number = {7}, - pages = {18--28}, - publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, +@techreport{oecd2023blueprint, + author = {Oecd}, + title = {A blueprint for building national compute capacity for artificial intelligence}, + year = {2023}, + number = {350}, + url = {https://doi.org/10.1787/876367e3-en}, + doi = {10.1787/876367e3-en}, source = {Crossref}, - title = {The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink}, - url = {https://doi.org/10.1109/mc.2022.3148714}, - volume = {55}, - year = {2022} + institution = {Organisation for Economic Co-Operation and Development (OECD)}, + issn = {2071-6826}, + month = feb, } @article{patterson2022carbon, @@ -454,7 +479,8 @@ @article{patterson2022carbon title = {The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink}, url = {https://doi.org/10.1109/mc.2022.3148714}, volume = {55}, - year = {2022} + year = {2022}, + month = jul, } @article{phillips2020four, @@ -462,23 +488,28 @@ @article{phillips2020four journal = {Gaithersburg, Maryland}, title = {Four principles of explainable artificial intelligence}, volume = {18}, - year = {2020} + year = {2020}, } @article{poff2002aquatic, author = {LeRoy Poff, N and Brinson, MM and Day, JW}, journal = {Pew Center on Global Climate Change}, title = {Aquatic ecosystems \& Global climate change}, - year = {2002} + year = {2002}, } @inproceedings{prakash2022cfu, author = {Prakash, Shvetank and Callahan, Tim and Bushagour, Joseph and Banbury, Colby and Green, Alan V. and Warden, Pete and Ansell, Tim and Reddi, Vijay Janapa}, journal = {ArXiv preprint}, title = {{CFU} Playground: {Full-stack} Open-Source Framework for Tiny Machine Learning {(TinyML)} Acceleration on {FPGAs}}, - url = {https://arxiv.org/abs/2201.01863}, + url = {https://doi.org/10.1109/ispass57527.2023.00024}, volume = {abs/2201.01863}, - year = {2022} + year = {2023}, + doi = {10.1109/ispass57527.2023.00024}, + source = {Crossref}, + booktitle = {2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)}, + publisher = {IEEE}, + month = apr, } @misc{prakash2023tinyml, @@ -487,7 +518,7 @@ @misc{prakash2023tinyml title = {Is {TinyML} Sustainable? Assessing the Environmental Impacts of Machine Learning on Microcontrollers}, url = {https://arxiv.org/abs/2301.11899}, volume = {abs/2301.11899}, - year = {2023} + year = {2023}, } @article{schwartz2020green, @@ -502,7 +533,8 @@ @article{schwartz2020green title = {Green {AI}}, url = {https://doi.org/10.1145/3381831}, volume = {63}, - year = {2020} + year = {2020}, + month = nov, } @inproceedings{schwartz2021deployment, @@ -515,14 +547,15 @@ @inproceedings{schwartz2021deployment source = {Crossref}, title = {Deployment of Embedded Edge-{AI} for Wildlife Monitoring in Remote Regions}, url = {https://doi.org/10.1109/icmla52953.2021.00170}, - year = {2021} + year = {2021}, + month = dec, } @article{shehabi2016united, author = {Shehabi, Arman and Smith, Sarah and Sartor, Dale and Brown, Richard and Herrlin, Magnus and Koomey, Jonathan and Masanet, Eric and Horner, Nathaniel and Azevedo, In\^es and Lintner, William}, institution = {Berkeley Laboratory}, title = {United states data center energy usage report}, - year = {2016} + year = {2016}, } @article{siddik2021environmental, @@ -537,7 +570,8 @@ @article{siddik2021environmental title = {The environmental footprint of data centers in the United States}, url = {https://doi.org/10.1088/1748-9326/abfba1}, volume = {16}, - year = {2021} + year = {2021}, + month = may, } @article{silvestro2022improving, @@ -552,7 +586,8 @@ @article{silvestro2022improving title = {Improving biodiversity protection through artificial intelligence}, url = {https://doi.org/10.1038/s41893-022-00851-6}, volume = {5}, - year = {2022} + year = {2022}, + month = mar, } @article{singh2022disentangling, @@ -567,19 +602,21 @@ @article{singh2022disentangling title = {Disentangling the worldwide web of e-waste and climate change co-benefits}, url = {https://doi.org/10.1016/j.cec.2022.100011}, volume = {1}, - year = {2022} + year = {2022}, + month = dec, } @inproceedings{strubell2019energy, + author = {Strubell, Emma and Ganesh, Ananya and McCallum, Andrew}, address = {Florence, Italy}, - author = {Strubell, Emma and Ganesh, Ananya and McCallum, Andrew}, booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics}, - doi = {10.18653/v1/P19-1355}, + doi = {10.18653/v1/p19-1355}, pages = {3645--3650}, publisher = {Association for Computational Linguistics}, title = {Energy and Policy Considerations for Deep Learning in {NLP}}, - url = {https://aclanthology.org/P19-1355}, - year = {2019} + url = {https://doi.org/10.18653/v1/p19-1355}, + year = {2019}, + source = {Crossref}, } @article{sudhakar2023data, @@ -594,7 +631,8 @@ @article{sudhakar2023data title = {Data Centers on Wheels: {Emissions} From Computing Onboard Autonomous Vehicles}, url = {https://doi.org/10.1109/mm.2022.3219803}, volume = {43}, - year = {2023} + year = {2023}, + month = jan, } @article{thompson2021deep, @@ -609,7 +647,8 @@ @article{thompson2021deep title = {Deep Learning's Diminishing Returns: {The} Cost of Improvement is Becoming Unsustainable}, url = {https://doi.org/10.1109/mspec.2021.9563954}, volume = {58}, - year = {2021} + year = {2021}, + month = oct, } @article{till2019fish, @@ -624,12 +663,13 @@ @article{till2019fish title = {Fish die-offs are concurrent with thermal extremes in north temperate lakes}, url = {https://doi.org/10.1038/s41558-019-0520-y}, volume = {9}, - year = {2019} + year = {2019}, + month = jul, } @article{uddin2012energy, - abstract = {The increasing demand for storage, networking and computation has driven intensification of large complex data centers that run many of today's Internet, financial, commercial and business applications. A data center comprises of many thousands of servers and can use as much energy as small city. Massive amount of computation power is required to drive and run these server farms resulting in many challenging like huge energy consumptions, emission of green house gases, backups and recovery; This paper proposes energy efficiency and low carbon enabler green IT framework for these large and complex server farms to save consumption of electricity and reduce the emission of green house gases to lower the effects of global warming. The framework uses latest energy saving techniques like virtualization, cloud computing and green metrics to achieve greener data centers. It comprises of five phase to properly implement green IT techniques to achieve green data centers. The proposed framework seamlessly divides data center components into different resource pools and then applies green metrics like Power Usage Effectiveness, Data Center Effectiveness and Carbon Emission Calculator to measure performance of individual components so that benchmarking values can be achieved and set as standard to be followed by data centers.}, author = {Uddin, Mueen and Rahman, Azizah Abdul}, + abstract = {The increasing demand for storage, networking and computation has driven intensification of large complex data centers that run many of today's Internet, financial, commercial and business applications. A data center comprises of many thousands of servers and can use as much energy as small city. Massive amount of computation power is required to drive and run these server farms resulting in many challenging like huge energy consumptions, emission of green house gases, backups and recovery; This paper proposes energy efficiency and low carbon enabler green IT framework for these large and complex server farms to save consumption of electricity and reduce the emission of green house gases to lower the effects of global warming. The framework uses latest energy saving techniques like virtualization, cloud computing and green metrics to achieve greener data centers. It comprises of five phase to properly implement green IT techniques to achieve green data centers. The proposed framework seamlessly divides data center components into different resource pools and then applies green metrics like Power Usage Effectiveness, Data Center Effectiveness and Carbon Emission Calculator to measure performance of individual components so that benchmarking values can be achieved and set as standard to be followed by data centers.}, doi = {10.1016/j.rser.2012.03.014}, issn = {1364-0321}, journal = {Renewable Sustainable Energy Rev.}, @@ -641,7 +681,8 @@ @article{uddin2012energy title = {Energy efficiency and low carbon enabler green {IT} framework for data centers considering green metrics}, url = {https://doi.org/10.1016/j.rser.2012.03.014}, volume = {16}, - year = {2012} + year = {2012}, + month = aug, } @book{un2019circular, @@ -649,7 +690,7 @@ @book{un2019circular publisher = {PACE - Platform for Accelerating the Circular Economy}, title = {A New Circular Vision for Electronics, Time for a Global Reboot}, url = {https://www3.weforum.org/docs/WEF\_A\_New\_Circular\_Vision\_for\_Electronics.pdf}, - year = {2019} + year = {2019}, } @article{wald1987semiconductor, @@ -664,7 +705,8 @@ @article{wald1987semiconductor title = {Semiconductor manufacturing: {An} introduction to processes and hazards}, url = {https://doi.org/10.1002/ajim.4700110209}, volume = {11}, - year = {1987} + year = {1987}, + month = jan, } @article{wu2022sustainable, @@ -673,7 +715,7 @@ @article{wu2022sustainable pages = {795--813}, title = {Sustainable ai: {Environmental} implications, challenges and opportunities}, volume = {4}, - year = {2022} + year = {2022}, } @inproceedings{zafrir2019q8bert, @@ -686,7 +728,8 @@ @inproceedings{zafrir2019q8bert source = {Crossref}, title = {{Q8BERT:} {Quantized} {8Bit} {BERT}}, url = {https://doi.org/10.1109/emc2-nips53020.2019.00016}, - year = {2019} + year = {2019}, + month = dec, } @article{zhang2018review, @@ -701,14 +744,6 @@ @article{zhang2018review title = {Review on the research and practice of deep learning and reinforcement learning in smart grids}, url = {https://doi.org/10.17775/cseejpes.2018.00520}, volume = {4}, - year = {2018} + year = {2018}, + month = sep, } - -@article{oecd2023blueprint, - author = "OECD", - title = "A blueprint for building national compute capacity for artificial intelligence", - year = "2023", - number = "350", - url = "https://www.oecd-ilibrary.org/content/paper/876367e3-en", - doi = "https://doi.org/https://doi.org/10.1787/876367e3-en" -} \ No newline at end of file diff --git a/contents/training/training.bib b/contents/training/training.bib index d5591dee..bf0ef6cf 100644 --- a/contents/training/training.bib +++ b/contents/training/training.bib @@ -1,3 +1,6 @@ +%comment{This file was created with betterbib v5.0.11.} + + @article{dahl2023benchmarking, author = {Dahl, George E and Schneider, Frank and Nado, Zachary and Agarwal, Naman and Sastry, Chandramouli Shama and Hennig, Philipp and Medapati, Sourabh and Eschenhagen, Runa and Kasimbeg, Priya and Suo, Daniel and others}, doi = {10.1212/nxi.0000000000001086}, @@ -9,19 +12,20 @@ @article{dahl2023benchmarking title = {{CSF} Findings in Acute {NMDAR} and {LGI1} {Antibody{\textendash}Associated} Autoimmune Encephalitis}, url = {https://doi.org/10.1212/nxi.0000000000001086}, volume = {8}, - year = {2021} + year = {2021}, + month = nov, } @inproceedings{diederik2015adam, - author = {Diederik P. Kingma and Jimmy Ba}, + author = {Kingma, Diederik P. and Ba, Jimmy}, + editor = {Bengio, Yoshua and LeCun, Yann}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/journals/corr/KingmaB14.bib}, - booktitle = {3rd International Conference on Learning Representations, {ICLR} 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings}, - editor = {Yoshua Bengio and Yann LeCun}, + booktitle = {3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings}, timestamp = {Thu, 25 Jul 2019 01:00:00 +0200}, title = {Adam: {A} Method for Stochastic Optimization}, url = {http://arxiv.org/abs/1412.6980}, - year = {2015} + year = {2015}, } @inproceedings{glorot2010understanding, @@ -29,7 +33,7 @@ @inproceedings{glorot2010understanding booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics}, title = {Understanding the difficulty of training deep feedforward neural networks}, url = {https://proceedings.mlr.press/v9/glorot10a.html}, - year = {2010} + year = {2010}, } @misc{hinton2017overview, @@ -37,48 +41,50 @@ @misc{hinton2017overview howpublished = {University Lecture}, institution = {University of Toronto}, title = {Overview of Minibatch Gradient Descent}, - year = {2017} + year = {2017}, } @inproceedings{jasper2012practical, - author = {Jasper Snoek and Hugo Larochelle and Ryan P. Adams}, + author = {Snoek, Jasper and Larochelle, Hugo and Adams, Ryan P.}, + editor = {Bartlett, Peter L. and Pereira, Fernando C. N. and Burges, Christopher J. C. and Bottou, L\'eon and Weinberger, Kilian Q.}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/nips/SnoekLA12.bib}, booktitle = {Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States}, - editor = {Peter L. Bartlett and Fernando C. N. Pereira and Christopher J. C. Burges and L{\'{e}}on Bottou and Kilian Q. Weinberger}, pages = {2960--2968}, timestamp = {Thu, 21 Jan 2021 00:00:00 +0100}, - title = {Practical Bayesian Optimization of Machine Learning Algorithms}, + title = {Practical {Bayesian} Optimization of Machine Learning Algorithms}, url = {https://proceedings.neurips.cc/paper/2012/hash/05311655a15b75fab86956663e1819cd-Abstract.html}, - year = {2012} + year = {2012}, } @inproceedings{john2010adaptive, - author = {John C. Duchi and Elad Hazan and Yoram Singer}, + author = {Duchi, John C. and Hazan, Elad and Singer, Yoram}, + editor = {Kalai, Adam Tauman and Mohri, Mehryar}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/colt/DuchiHS10.bib}, - booktitle = {{COLT} 2010 - The 23rd Conference on Learning Theory, Haifa, Israel, June 27-29, 2010}, - editor = {Adam Tauman Kalai and Mehryar Mohri}, + booktitle = {COLT 2010 - The 23rd Conference on Learning Theory, Haifa, Israel, June 27-29, 2010}, pages = {257--269}, publisher = {Omnipress}, timestamp = {Tue, 19 Feb 2013 00:00:00 +0100}, title = {Adaptive Subgradient Methods for Online Learning and Stochastic Optimization}, url = {http://colt2010.haifa.il.ibm.com/papers/COLT2010proceedings.pdf\#page=265}, - year = {2010} + year = {2010}, } @inproceedings{kaiming2015delving, - author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun}, + author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/iccv/HeZRS15.bib}, - booktitle = {2015 {IEEE} International Conference on Computer Vision, {ICCV} 2015, Santiago, Chile, December 7-13, 2015}, - doi = {10.1109/ICCV.2015.123}, + booktitle = {2015 IEEE International Conference on Computer Vision (ICCV)}, + doi = {10.1109/iccv.2015.123}, pages = {1026--1034}, - publisher = {{IEEE} Computer Society}, + publisher = {IEEE}, timestamp = {Wed, 17 Apr 2019 01:00:00 +0200}, - title = {Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification}, - url = {https://doi.org/10.1109/ICCV.2015.123}, - year = {2015} + title = {Delving Deep into Rectifiers: {Surpassing} Human-Level Performance on {ImageNet} Classification}, + url = {https://doi.org/10.1109/iccv.2015.123}, + year = {2015}, + source = {Crossref}, + month = dec, } @article{robbins1951stochastic, @@ -93,7 +99,8 @@ @article{robbins1951stochastic title = {A Stochastic Approximation Method}, url = {https://doi.org/10.1214/aoms/1177729586}, volume = {22}, - year = {1951} + year = {1951}, + month = sep, } @article{ruder2016overview, @@ -102,7 +109,7 @@ @article{ruder2016overview title = {An overview of gradient descent optimization algorithms}, url = {https://arxiv.org/abs/1609.04747}, volume = {abs/1609.04747}, - year = {2016} + year = {2016}, } @article{srivastava2014dropout, @@ -110,39 +117,39 @@ @article{srivastava2014dropout journal = {J. Mach. Learn. Res.}, title = {Dropout: {A} Simple Way to Prevent Neural Networks from Overfitting}, url = {http://jmlr.org/papers/v15/srivastava14a.html}, - year = {2014} + year = {2014}, } @misc{torsten2021sparsity, + author = {Hoefler, Torsten and Alistarh, Dan and Ben-Nun, Tal and Dryden, Nikoli and Peste, Alexandra}, archiveprefix = {arXiv}, - author = {Torsten Hoefler and Dan Alistarh and Tal Ben-Nun and Nikoli Dryden and Alexandra Peste}, eprint = {2102.00554}, primaryclass = {cs.LG}, - title = {Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks}, - year = {2021} + title = {Sparsity in Deep Learning: {Pruning} and growth for efficient inference and training in neural networks}, + year = {2021}, } @misc{yang2018imagenet, + author = {You, Yang and Zhang, Zhao and Hsieh, Cho-Jui and Demmel, James and Keutzer, Kurt}, archiveprefix = {arXiv}, - author = {Yang You and Zhao Zhang and Cho-Jui Hsieh and James Demmel and Kurt Keutzer}, eprint = {1709.05011}, primaryclass = {cs.CV}, - title = {ImageNet Training in Minutes}, - year = {2018} + title = {{ImageNet} Training in Minutes}, + year = {2018}, } @misc{you2018imagenet, - archiveprefix = {arXiv}, author = {You, Yang and Zhang, Zhao and Hsieh, Cho-Jui and Demmel, James and Keutzer, Kurt}, + archiveprefix = {arXiv}, eprint = {1709.05011}, primaryclass = {cs.CV}, title = {{ImageNet} Training in Minutes}, - year = {2018} + year = {2018}, } @misc{zeiler2012reinforcement, - archiveprefix = {arXiv}, author = {Zeiler, Matthew D.}, + archiveprefix = {arXiv}, doi = {10.1002/9781118266502.ch6}, eprint = {1212.5701}, isbn = {9780470919996, 9781118266502}, @@ -152,12 +159,13 @@ @misc{zeiler2012reinforcement source = {Crossref}, title = {Reinforcement and Systemic Machine Learning for Decision Making}, url = {https://doi.org/10.1002/9781118266502.ch6}, - year = {2012} + year = {2012}, + month = jul, } @misc{zoph2023cybernetical, - archiveprefix = {arXiv}, author = {Zoph, Barret and Le, Quoc V.}, + archiveprefix = {arXiv}, doi = {10.1002/9781394217519.ch17}, eprint = {1611.01578}, isbn = {9781394217489, 9781394217519}, @@ -167,5 +175,6 @@ @misc{zoph2023cybernetical source = {Crossref}, title = {Cybernetical Intelligence}, url = {https://doi.org/10.1002/9781394217519.ch17}, - year = {2023} + year = {2023}, + month = oct, } diff --git a/contents/workflow/workflow.bib b/contents/workflow/workflow.bib index e69de29b..00614696 100644 --- a/contents/workflow/workflow.bib +++ b/contents/workflow/workflow.bib @@ -0,0 +1,2 @@ +%comment{This file was created with betterbib v5.0.11.} +