A Survey on Modeling and Improving Reliability of DNN Algorithms and Accelerators

Mittal, Sparsh (2019) A Survey on Modeling and Improving Reliability of DNN Algorithms and Accelerators. Journal of Systems Architecture, 104. ISSN 1383-7621

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As DNNs become increasingly common in mission-critical applications, ensuring their reliable operation has become crucial. Conventional resilience techniques fail to account for the unique characteristics of DNN algorithms/accelerators, and hence, they are infeasible or ineffective. In this paper, we present a survey of techniques for studying and optimizing the reliability of DNN accelerators and architectures. The reliability issues we cover include soft/hard errors arising due to process variation, voltage scaling, timing errors, DRAM errors due to refresh rate scaling and thermal effects, etc. We organize the research projects on several categories to bring out their key attributes. This paper underscores the importance of designing for reliability as the first principle, and not merely retrofit for it.

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IITH Creators:
IITH CreatorsORCiD
Mittal, Sparshhttp://orcid.org/0000-0002-2908-993X
Item Type: Article
Uncontrolled Keywords: Deep learning, Deep neural networks, Fault-injection, Permanent fault, Review, Transient fault, Indexed in Scopus
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 02 Dec 2019 04:37
Last Modified: 14 Jan 2020 03:55
URI: http://raiith.iith.ac.in/id/eprint/7074
Publisher URL: http://doi.org/10.1016/j.sysarc.2019.101689
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