Abstract: A significant trend in machine learning is sparsifying the training of neural networks to reduce the amount of computation required.
Accelerating SLIDE: Exploiting Sparsity on Accelerator Architectures
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A significant trend in machine learning is sparsifying the training of neural networks to reduce the amount of computation required.
Abstract—A significant trend in machine learning is sparsifying the training of neural networks to reduce the amount of computation required.
Accelerating SLIDE: Exploiting Sparsity on Accelerator Architectures. 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW).
Accelerating SLIDE: Exploiting Sparsity on Accelerator Architectures. IPDPS ... Gorgon: Accelerating Machine Learning from Relational Data. ISCA 2020 ...
of sparsity in training. •To support our sparse dataflow, an accelerator architecture is. designed for CNNs training that can benefit from both activation.
Accelerating SLIDE: Exploiting Sparsity on Accelerator Architectures. S Ko, A Rucker, Y Zhang, P Mure, K Olukotun. 2022 IEEE International Parallel and ...
• As always, must find a design where benefits of exploiting the sparsity exceeds the overhead. • Achieving gains is particularly challenging when the amount ...
Accelerating SLIDE: Exploiting Sparsity on Accelerator Architectures. Sho Ko, Alexander Rucker, Yaqi Zhang, Paul Mure, Kunle Olukotun. 2022 IEEE International ...
Jul 15, 2023 · In this paper, we highlight the challenges associated with exploiting post-activation sparsity for performance gains in streaming CNN ...
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