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arXiv · 2210.06696

CPSAA: Accelerating Sparse Attention using Crossbar-based Processing-In-Memory Architecture

Huize Li·Hai Jin·Long Zheng·Yu Huang·Xiaofei Liao·Dan Chen·Zhuohui Duan·Cong Liu·Jiahong Xu·Chuanyi Gui
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Abstract

The attention mechanism requires huge computational efforts to process unnecessary calculations, significantly limiting the system's performance. Researchers propose sparse attention to convert some DDMM operations to SDDMM and SpMM operations. However, current sparse attention solutions introduce massive off-chip random memory access. We propose CPSAA, a novel crossbar-based PIM-featured sparse attention accelerator. First, we present a novel attention calculation mode. Second, we design a novel PIM-based sparsity pruning architecture. Finally, we present novel crossbar-based methods. Experimental results show that CPSAA has an average of 89.6X, 32.2X, 17.8X, 3.39X, and 3.84X performance improvement and 755.6X, 55.3X, 21.3X, 5.7X, and 4.9X energy-saving when compare with GPU, FPGA, SANGER, ReBERT, and ReTransformer.

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Huize Li, Hai Jin, Long Zheng, Yu Huang, Xiaofei Liao, Dan Chen, Zhuohui Duan, Cong Liu, Jiahong Xu, Chuanyi Gui. 2022-10-13. CPSAA: Accelerating Sparse Attention using Crossbar-based Processing-In-Memory Architecture. https://arxiv.org/abs/2210.06696

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