Sparse-PE: A Performance-Efficient Processing Engine Core for Sparse Convolutional Neural Networks
Sparse convolutional neural network (CNN) models reduce the massive compute and memory bandwidth requirements inherently present in dense CNNs without a significant loss in accuracy. Sparse CNNs, however, present their own set of challenges including non-linear data accesses and complex design of CN...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/cb34629c94244243822ec50d59d8eb02 |
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| Sumario: | Sparse convolutional neural network (CNN) models reduce the massive compute and memory bandwidth requirements inherently present in dense CNNs without a significant loss in accuracy. Sparse CNNs, however, present their own set of challenges including non-linear data accesses and complex design of CNN processing elements (PEs). Recently proposed accelerators like SCNN, Eyeriss v2, and SparTen, exploit the <italic>two-sided</italic> sparsity, that is, sparsity in both the input activations and weights to accelerate the CNN inference. These, accelerators, however, suffer from a multitude of problems that limit their applicability, such as inefficient micro-architecture (SCNN, Eyeriss v2), complex PE design (Eyeriss v2), no support for non-unit stride convolutions (SCNN) and FC layers (SparTen, SCNN). To address these issues in contemporary sparse CNN accelerators, we propose <italic>Sparse-PE</italic>, a multi-threaded, and flexible CNN PE, capable of handling both the dense and sparse CNNs. The Sparse-PE core uses binary mask representation and actively skips computations involving zeros and favors non-zero computations, thereby, drastically increasing the effective throughput and hardware utilization. Unlike previous designs, the Sparse-PE core is generic in nature and not targeted towards a specific accelerator, and thus, can also be used as a standalone sparse dot product compute engine. We evaluate the performance of the core using a custom built cycle accurate simulator. Our simulations show that the Sparse-PE core-based accelerator provides a performance gain of <inline-formula> <tex-math notation="LaTeX">$12\times $ </tex-math></inline-formula> over a recently proposed dense accelerator (NeuroMAX). For sparse accelerators, it provides a performance gain of <inline-formula> <tex-math notation="LaTeX">$4.2\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$2.38\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$1.98\times $ </tex-math></inline-formula> over SCNN, Eyeriss v2, and SparTen, respectively. |
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