On-Line Fault Protection for ReRAM-Based Neural Networks

doi:10.1109/TC.2022.3160345

	On-Line Fault Protection for ReRAM-Based Neural Networks
	Li, Wen 1,2; Wang, Ying 1,2; Liu, Cheng 1,2; He, Yintao 1,2; Liu, Lian 1,2; Li, Huawei 1,3; Li, Xiaowei 1,2
	2023-02-01
发表期刊	IEEE TRANSACTIONS ON COMPUTERS
ISSN	0018-9340
卷号	72 期号:2 页码:423-437
摘要	The emerging Resistive RAM (ReRAM) technology significantly boosts the performance and the energy efficiency of the deep learning accelerators (DLAs) via the Computing-in-Memory (CiM) architecture. However, ReRAM-based DLA also suffers a high occurrence rate of memory faults. How to detect and protect against the faults in ReRAM devices poses great challenges to ReRAM-based DLA design. In this work, we propose RRAMedy, an in-situ fault detection and network remedy framework for ReRAM-based DLAs. With the proposed Adversarial Example Testing, which is a lifetime on-device and on-line fault detection technique, it achieves high detection coverage of both hard faults and soft faults at a low run-time cost. In addition, it employs an edge-cloud collaborative model retraining method to tolerate the detected faults by leveraging the inherent fault-adaptive capability of DNNs. Meanwhile, to enable in-situ model remedy when the cloud assistance is absent due to security or overhead issues, we propose to accelerate the fault-masking retraining process on edge devices with parallelized Knowledge Transfer. Our experimental results show that the proposed fault detection technique achieves high fault detection accuracy and delivers real-time testing performance. Meanwhile, the proposed retraining approach greatly alleviates the accuracy degradation problem and achieves excellent performance speedups over the baselines.
关键词	Training Fault detection Computational modeling Image edge detection Memristors Neural networks Kernel Deep neural network hard fault ReRAM reliability soft fault
DOI	10.1109/TC.2022.3160345
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] ; Zhejiang Lab[2021PC0AC01]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Hardware & Architecture ; Engineering, Electrical & Electronic
WOS记录号	WOS:000917782600010
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/2XEOYT63/19936
专题	中国科学院计算技术研究所期刊论文
通讯作者	Wang, Ying
作者单位	1.Chinese Acad Sci, Inst Comp Technol, SKLCA, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China 3.Peng Cheng Lab, Shenzhen 518066, Peoples R China
推荐引用方式 GB/T 7714	Li, Wen,Wang, Ying,Liu, Cheng,et al. On-Line Fault Protection for ReRAM-Based Neural Networks[J]. IEEE TRANSACTIONS ON COMPUTERS,2023,72(2):423-437.
APA	Li, Wen.,Wang, Ying.,Liu, Cheng.,He, Yintao.,Liu, Lian.,...&Li, Xiaowei.(2023).On-Line Fault Protection for ReRAM-Based Neural Networks.IEEE TRANSACTIONS ON COMPUTERS,72(2),423-437.
MLA	Li, Wen,et al."On-Line Fault Protection for ReRAM-Based Neural Networks".IEEE TRANSACTIONS ON COMPUTERS 72.2(2023):423-437.