Saving Energy of RRAM-Based Neural Accelerator Through State-Aware Computing

doi:10.1109/TCAD.2021.3103147

	Saving Energy of RRAM-Based Neural Accelerator Through State-Aware Computing
	He, Yintao 1,2; Wang, Ying 1,2; Li, Huawei 1,2,3; Li, Xiaowei 1,2
	2022-07-01
发表期刊	IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
ISSN	0278-0070
卷号	41 期号:7 页码:2115-2127
摘要	In-memory computing (IMC) is recognized as one of the most promising architecture solution to realize energy-efficient neural network inference. Amongst many memory technology, resistive RAM (RRAM) is a very attractive device to implement the IMC-based neural network accelerator architecture, which is particularly suitable for power-constrained IoT systems. Due to the nature of low leakage and in-situ computing, the dynamic power consumption of dot-production operations in RRAM crossbars dominates the chip power, especially when applied to low-precision neural networks. This work investigates the correlation between the cell resistance state and the crossbar operation power, and proposes a state-aware RRAM accelerator (SARA) architecture for energy-efficient low-precision neural networks. With the proposed state-aware network training and mapping strategy, crossbars in the RRAM accelerator can perform in a lower power state. Furthermore, we also leverage the proposed RRAM accelerator architecture to reduce the power consumption of high-precision network inference with both single-level or multilevel RRAM. The evaluation results show that for binary neural networks, our design saves 40.53% RRAM computing energy on average over the baseline. For high precision neural networks, the proposed method reduces 11.67% computing energy on average without any accuracy loss.
关键词	Computer architecture Microprocessors Resistance Power demand Training Biological neural networks Optimization Low power (LP) neural networks processing-in-memory resistive random-access memory (RRAM)
DOI	10.1109/TCAD.2021.3103147
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2020YFB1600201] ; National Natural Science Foundation of China (NSFC)[62090024] ; National Natural Science Foundation of China (NSFC)[61874124] ; National Natural Science Foundation of China (NSFC)[61876173]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Interdisciplinary Applications ; Engineering, Electrical & Electronic
WOS记录号	WOS:000812532700015
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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/2XEOYT63/19627
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Ying
作者单位	1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 3.Peng Cheng Lab, Shenzhen 518066, Peoples R China
推荐引用方式 GB/T 7714	He, Yintao,Wang, Ying,Li, Huawei,et al. Saving Energy of RRAM-Based Neural Accelerator Through State-Aware Computing[J]. IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,2022,41(7):2115-2127.
APA	He, Yintao,Wang, Ying,Li, Huawei,&Li, Xiaowei.(2022).Saving Energy of RRAM-Based Neural Accelerator Through State-Aware Computing.IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,41(7),2115-2127.
MLA	He, Yintao,et al."Saving Energy of RRAM-Based Neural Accelerator Through State-Aware Computing".IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 41.7(2022):2115-2127.