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

doi:10.1109/TC.2024.3371790

	GAS: General-Purpose In-Memory-Computing Accelerator for Sparse Matrix Multiplication
	Zhang, Xiaoyu 1,2; Li, Zerun 1,2; Liu, Rui 1,3; Chen, Xiaoming 1,2; Han, Yinhe 1,2
	2024-06-01
发表期刊	IEEE TRANSACTIONS ON COMPUTERS
ISSN	0018-9340
卷号	73 期号:6 页码:1427-1441
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.
关键词	Sparse matrices FeFETs Computer architecture Arrays Nonvolatile memory Microprocessors Vectors Sparse matrix multiplication in-memory computing SpMV SpMSpV SpMM SpMSpM
DOI	10.1109/TC.2024.3371790
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Hardware & Architecture ; Engineering, Electrical & Electronic
WOS记录号	WOS:001218445400008
出版者	IEEE COMPUTER SOC
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38971
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.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, 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.