adding references for 2:4 pruning

hw_acceleration.qmd

@@ -526,11 +526,11 @@ guide software optimizations, while algorithmic advances inform hardware
 specialization. This mutual enhancement provides multiplicative
 efficiency gains compared to isolated efforts.
 
-#### Algorithm-Hardare Co-exploration
+#### Algorithm-Hardware Co-exploration
 
 Jointly exploring innovations in neural network architectures along with custom hardware design is a powerful co-design technique. This allows finding ideal pairings tailored to each other's strengths [@sze2017efficient].
 
-For instance, the shift to mobile architectures like MobileNets [@howard_mobilenets_2017] was guided by edge device constraints like model size and latency. The quantization [@jacob2018quantization] and pruning techniques [@gale2019state] that unlocked these efficient models became possible thanks to hardware accelerators with native low-precision integer support.
+For instance, the shift to mobile architectures like MobileNets [@howard_mobilenets_2017] was guided by edge device constraints like model size and latency. The quantization [@jacob2018quantization] and pruning techniques [@gale2019state] that unlocked these efficient models became possible thanks to hardware accelerators with native low-precision integer support and pruning support[@mishrapruning].
 
 Attention-based models have thrived on massively parallel GPUs and ASICs where their computation maps well spatially, as opposed to RNN architectures reliant on sequential processing. Co-evolution of algorithms and hardware unlocked new capabilities.
 

references.bib

@@ -4669,3 +4669,24 @@ @article{zhuang2020comprehensive
 	number       = 1,
 	pages        = {43--76}
 }
+
+@article{mishrapruning,
+  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},
+  title        = {Accelerating Sparse Deep Neural Networks},
+  journal      = {CoRR},
+  volume       = {abs/2104.08378},
+  year         = {2021},
+  url          = {https://arxiv.org/abs/2104.08378},
+  eprinttype    = {arXiv},
+  eprint       = {2104.08378},
+  timestamp    = {Mon, 26 Apr 2021 17:25:10 +0200},
+  biburl       = {https://dblp.org/rec/journals/corr/abs-2104-08378.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}