Merge branch 'dev' into mlperf-power

_quarto.yml

@@ -9,7 +9,7 @@ website:
   announcement: 
     icon: star-half
     dismissable: false
-    content: 🌟 Help Us Reach 1,000 GitHub Stars! 🌟 For every 25 stars, Arduino and SEEED will donate a NiclaVision or XIAO ESP32S3 for AI education. <a href="https://github.com/harvard-edge/cs249r_book">Click here to ⭐</a>
+    content: 🌟 **We Hit 1,000 GitHub Stars - Thank You!** 🌟 Thanks to you, Arduino and SEEED are donating NiclaVision and XIAO ESP32S3 boards for AI education. Let's keep going! Every additional 25 stars helps us bring more resources to the community. <a href="https://github.com/harvard-edge/cs249r_book">Click here to ⭐</a>
     type: info
     position: below-navbar
 
@@ -93,47 +93,47 @@ book:
     - part: FRONT MATTER
       chapters:
       - index.qmd
-      - contents/dedication.qmd
-      - contents/acknowledgements/acknowledgements.qmd
-      - contents/contributors.qmd
-      - contents/copyright.qmd
-      - contents/about.qmd
+      - contents/core/dedication.qmd
+      - contents/core/acknowledgements/acknowledgements.qmd
+      - contents/core/contributors.qmd
+      - contents/core/copyright.qmd
+      - contents/core/about.qmd
     - text: "---"
     - part: MAIN
     - part: Fundamentals
       chapters:
-      - contents/introduction/introduction.qmd
-      - contents/ml_systems/ml_systems.qmd
-      - contents/dl_primer/dl_primer.qmd
+      - contents/core/introduction/introduction.qmd
+      - contents/core/ml_systems/ml_systems.qmd
+      - contents/core/dl_primer/dl_primer.qmd
     - part: Workflow
       chapters:
-      - contents/workflow/workflow.qmd
-      - contents/data_engineering/data_engineering.qmd
-      - contents/frameworks/frameworks.qmd
+      - contents/core/workflow/workflow.qmd
+      - contents/core/data_engineering/data_engineering.qmd
+      - contents/core/frameworks/frameworks.qmd
     - part: Training
       chapters:
-      - contents/training/training.qmd
-      - contents/efficient_ai/efficient_ai.qmd
-      - contents/optimizations/optimizations.qmd
-      - contents/hw_acceleration/hw_acceleration.qmd
+      - contents/core/training/training.qmd
+      - contents/core/efficient_ai/efficient_ai.qmd
+      - contents/core/optimizations/optimizations.qmd
+      - contents/core/hw_acceleration/hw_acceleration.qmd
     - part: Deployment
       chapters:
-      - contents/benchmarking/benchmarking.qmd
-      - contents/ondevice_learning/ondevice_learning.qmd
-      - contents/ops/ops.qmd
+      - contents/core/benchmarking/benchmarking.qmd
+      - contents/core/ondevice_learning/ondevice_learning.qmd
+      - contents/core/ops/ops.qmd
     - part: Advanced Topics
       chapters:
-      - contents/privacy_security/privacy_security.qmd
-      - contents/responsible_ai/responsible_ai.qmd
-      - contents/sustainable_ai/sustainable_ai.qmd
-      - contents/robust_ai/robust_ai.qmd
-      - contents/generative_ai/generative_ai.qmd
+      - contents/core/privacy_security/privacy_security.qmd
+      - contents/core/responsible_ai/responsible_ai.qmd
+      - contents/core/sustainable_ai/sustainable_ai.qmd
+      - contents/core/robust_ai/robust_ai.qmd
+      - contents/core/generative_ai/generative_ai.qmd
     - part: Social Impact
       chapters:
-      - contents/ai_for_good/ai_for_good.qmd
+      - contents/core/ai_for_good/ai_for_good.qmd
     - part: Closing
       chapters:
-      - contents/conclusion/conclusion.qmd
+      - contents/core/conclusion/conclusion.qmd
     - text: "---"  
     - part: LABS
       chapters:
@@ -169,35 +169,35 @@ book:
         - references.qmd
     - text: "---"  
   appendices:
-      - contents/tools.qmd
-      - contents/zoo_datasets.qmd
-      - contents/zoo_models.qmd
-      - contents/learning_resources.qmd
-      - contents/community.qmd
-      - contents/case_studies.qmd
+      - contents/core/tools.qmd
+      - contents/core/zoo_datasets.qmd
+      - contents/core/zoo_models.qmd
+      - contents/core/learning_resources.qmd
+      - contents/core/community.qmd
+      - contents/core/case_studies.qmd
 
 bibliography: 
   # main
-  - contents/introduction/introduction.bib
-  - contents/ai_for_good/ai_for_good.bib
-  - contents/benchmarking/benchmarking.bib
-  - contents/data_engineering/data_engineering.bib
-  - contents/dl_primer/dl_primer.bib
-  - contents/efficient_ai/efficient_ai.bib
-  - contents/ml_systems/ml_systems.bib
-  - contents/frameworks/frameworks.bib
-  - contents/generative_ai/generative_ai.bib
-  - contents/hw_acceleration/hw_acceleration.bib
-  - contents/ondevice_learning/ondevice_learning.bib
-  - contents/ops/ops.bib
-  - contents/optimizations/optimizations.bib
-  - contents/privacy_security/privacy_security.bib
-  - contents/responsible_ai/responsible_ai.bib
-  - contents/robust_ai/robust_ai.bib
-  - contents/sustainable_ai/sustainable_ai.bib
-  - contents/training/training.bib
-  - contents/workflow/workflow.bib
-  - contents/conclusion/conclusion.bib
+  - contents/core/introduction/introduction.bib
+  - contents/core/ai_for_good/ai_for_good.bib
+  - contents/core/benchmarking/benchmarking.bib
+  - contents/core/data_engineering/data_engineering.bib
+  - contents/core/dl_primer/dl_primer.bib
+  - contents/core/efficient_ai/efficient_ai.bib
+  - contents/core/ml_systems/ml_systems.bib
+  - contents/core/frameworks/frameworks.bib
+  - contents/core/generative_ai/generative_ai.bib
+  - contents/core/hw_acceleration/hw_acceleration.bib
+  - contents/core/ondevice_learning/ondevice_learning.bib
+  - contents/core/ops/ops.bib
+  - contents/core/optimizations/optimizations.bib
+  - contents/core/privacy_security/privacy_security.bib
+  - contents/core/responsible_ai/responsible_ai.bib
+  - contents/core/robust_ai/robust_ai.bib
+  - contents/core/sustainable_ai/sustainable_ai.bib
+  - contents/core/training/training.bib
+  - contents/core/workflow/workflow.bib
+  - contents/core/conclusion/conclusion.bib
 
 comments:
   giscus:

contents/ai_for_good/ai_for_good.qmd → contents/core/ai_for_good/ai_for_good.qmd

@@ -34,12 +34,12 @@ By aligning AI progress with human values, goals, and ethics, the ultimate goal
 
 To give ourselves a framework around which to think about AI for social good, we will be following the UN Sustainable Development Goals (SDGs). The UN SDGs are a collection of 17 global goals, shown in @fig-sdg, adopted by the United Nations in 2015 as part of the 2030 Agenda for Sustainable Development. The SDGs address global challenges related to poverty, inequality, climate change, environmental degradation, prosperity, and peace and justice.
 
+![United Nations Sustainable Development Goals (SDG). Source: [United Nations](https://sdgs.un.org/goals).](https://www.un.org/sustainabledevelopment/wp-content/uploads/2015/12/english_SDG_17goals_poster_all_languages_with_UN_emblem_1.png){#fig-sdg}
+
 What is special about the SDGs is that they are a collection of interlinked objectives designed to serve as a "shared blueprint for peace and prosperity for people and the planet, now and into the future." The SDGs emphasize sustainable development's interconnected environmental, social, and economic aspects by putting sustainability at their center.
 
 A recent study [@vinuesa2020role] highlights the influence of AI on all aspects of sustainable development, particularly on the 17 Sustainable Development Goals (SDGs) and 169 targets internationally defined in the 2030 Agenda for Sustainable Development. The study shows that AI can act as an enabler for 134 targets through technological improvements, but it also highlights the challenges of AI on some targets. The study shows that AI can benefit 67 targets when considering AI and societal outcomes. Still, it also warns about the issues related to the implementation of AI in countries with different cultural values and wealth.
 
-![United Nations Sustainable Development Goals (SDG). Source: [United Nations](https://sdgs.un.org/goals).](https://www.un.org/sustainabledevelopment/wp-content/uploads/2015/12/english_SDG_17goals_poster_all_languages_with_UN_emblem_1.png){#fig-sdg}
-
 In our book's context, TinyML could help advance at least some of these SDG goals.  
 
 * **Goal 1 - No Poverty:** TinyML could help provide low-cost solutions for crop monitoring to improve agricultural yields in developing countries.

contents/benchmarking/benchmarking.bib → contents/core/benchmarking/benchmarking.bib

@@ -71,6 +71,25 @@ @inproceedings{brown2020language
  year = {2020},
 }
 
+@article{10.1145/3467017,
+author = {Hooker, Sara},
+title = {The hardware lottery},
+year = {2021},
+issue_date = {December 2021},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+volume = {64},
+number = {12},
+issn = {0001-0782},
+url = {https://doi.org/10.1145/3467017},
+doi = {10.1145/3467017},
+abstract = {After decades of incentivizing the isolation of hardware, software, and algorithm development, the catalysts for closer collaboration are changing the paradigm.},
+journal = {Commun. ACM},
+month = nov,
+pages = {58–65},
+numpages = {8}
+}
+
 @inproceedings{chu2021discovering,
  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},