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GAS: General-Purpose In-Memory-Computing Accelerator for Sparse Matrix Multiplication

文献类型：期刊论文

作者Zhang, Xiaoyu1,2; Li, Zerun1,2; Liu, Rui2,3; Chen, Xiaoming1,2; Han, Yinhe1,2
刊名IEEE TRANSACTIONS ON COMPUTERS
出版日期2024-06-01
卷号73期号:6页码:1427-1441
关键词Sparse matrices FeFETs Computer architecture Arrays Nonvolatile memory Microprocessors Vectors Sparse matrix multiplication in-memory computing SpMV SpMSpV SpMM SpMSpM
ISSN号0018-9340
DOI10.1109/TC.2024.3371790
英文摘要Sparse matrix multiplication is widely used in various practical applications. Different accelerators have been proposed to speed up sparse matrix-dense vector multiplication (SpMV), sparse matrix-sparse vector multiplication (SpMSpV), sparse matrix-dense matrix multiplication (SpMM), and sparse matrix-sparse matrix multiplication (SpMSpM). The performance of traditional sparse matrix multiplication accelerators is typically bounded by memory access due to the poor data locality and irregular memory access. In-memory computing (IMC) is a promising technique to alleviate the memory bottleneck. Previous IMC studies are mostly focused on accelerating a single sparse matrix multiplication function. In this paper, we propose GAS, a general-purpose IMC accelerator for sparse matrix multiplication. GAS integrates non-volatile memory based content-addressable memory (CAM) arrays and multiply-add computation (MAC) arrays to support sparse matrices represented in the double-precision floating-point format. Using a unified outer product based multiplication methodology, GAS supports the acceleration of SpMV, SpMSpv, SpMM, and SpMSpM. We further propose four optimization techniques to speed up the computation of GAS. GAS achieves significant speedups and energy savings over central processing unit (CPU) and graphics processing unit (GPU) implementations. Compared with state-of-the-art traditional and IMC-based accelerators, GAS not only supports more functions, but also achieves higher performance and energy efficiency.
资助项目National Key Research and Development Program of China
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001218445400008
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/38970]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Chen, Xiaoming
作者单位1.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
3.Xiangtan Univ, Sch Mat Sci & Engn, Xiangtan 411105, Hunan, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Xiaoyu,Li, Zerun,Liu, Rui,et al. GAS: General-Purpose In-Memory-Computing Accelerator for Sparse Matrix Multiplication[J]. IEEE TRANSACTIONS ON COMPUTERS,2024,73(6):1427-1441.
APA Zhang, Xiaoyu,Li, Zerun,Liu, Rui,Chen, Xiaoming,&Han, Yinhe.(2024).GAS: General-Purpose In-Memory-Computing Accelerator for Sparse Matrix Multiplication.IEEE TRANSACTIONS ON COMPUTERS,73(6),1427-1441.
MLA Zhang, Xiaoyu,et al."GAS: General-Purpose In-Memory-Computing Accelerator for Sparse Matrix Multiplication".IEEE TRANSACTIONS ON COMPUTERS 73.6(2024):1427-1441.

入库方式： OAI收割

来源：计算技术研究所

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