Survey on Redundancy Based-Fault tolerance methods for Processors and Hardware accelerators - Trends in Quantum Computing, Heterogeneous Systems and Reliability

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-05-06 DOI:10.1145/3663672
Shashikiran Venkatesha, Ranjani Parthasarathi
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Abstract

Rapid progress in the CMOS technology for the past 25 years has increased the vulnerability of processors towards faults. Subsequently, focus of computer architects shifted towards designing fault-tolerance methods for processor architectures. Concurrently, chip designers encountered high order challenges for designing fault tolerant processor architectures. For processor cores, redundancy-based fault tolerance methods for fault detection at core level, micro-architectural level ,thread level , and software level are discussed. Similar applicable redundancy-based fault tolerance methods for cache memory, and hardware accelerators are presented in the article. Recent trends in fault tolerant quantum computing and quantum error correction are also discussed. The classification of state-of-the-art techniques is presented in the survey would help the researchers to organize their work on established lines.

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基于冗余的处理器和硬件加速器容错方法调查 - 量子计算、异构系统和可靠性的发展趋势
过去 25 年中,CMOS 技术的飞速发展增加了处理器对故障的脆弱性。因此,计算机架构师的工作重点转向为处理器架构设计容错方法。与此同时,芯片设计人员在设计容错处理器架构时也遇到了高阶挑战。针对处理器内核,讨论了在内核级、微体系结构级、线程级和软件级进行故障检测的基于冗余的容错方法。文章还介绍了类似的适用于高速缓冲存储器和硬件加速器的基于冗余的容错方法。文章还讨论了容错量子计算和量子纠错的最新趋势。调查中介绍的最新技术分类将有助于研究人员根据既定路线组织工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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