Computer Science > Hardware Architecture
[Submitted on 15 Jul 2023]
Title:PASS: Exploiting Post-Activation Sparsity in Streaming Architectures for CNN Acceleration
View PDFAbstract:With the ever-growing popularity of Artificial Intelligence, there is an increasing demand for more performant and efficient underlying hardware. Convolutional Neural Networks (CNN) are a workload of particular importance, which achieve high accuracy in computer vision applications. Inside CNNs, a significant number of the post-activation values are zero, resulting in many redundant computations. Recent works have explored this post-activation sparsity on instruction-based CNN accelerators but not on streaming CNN accelerators, despite the fact that streaming architectures are considered the leading design methodology in terms of performance. In this paper, we highlight the challenges associated with exploiting post-activation sparsity for performance gains in streaming CNN accelerators, and demonstrate our approach to address them. Using a set of modern CNN benchmarks, our streaming sparse accelerators achieve 1.41x to 1.93x efficiency (GOP/s/DSP) compared to state-of-the-art instruction-based sparse accelerators.
Submission history
From: Alexander Montgomerie-Corcoran [view email][v1] Sat, 15 Jul 2023 15:03:08 UTC (155 KB)
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