Paper Info
Title
A Survey on Modeling and Improving Reliability of DNN Algorithms and Accelerators
Abstract
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.
Year
DOI
Venue
2020
10.1016/j.sysarc.2019.101689
Journal of Systems Architecture
Keywords
DocType
Volume
Review,Deep neural networks,Permanent fault,Transient fault,Deep learning,Fault-injection
Journal
104
Issue
ISSN
Citations 
C
1383-7621
9
PageRank 
References 
Authors
0.60
0
1
Name
Order
Citations
PageRank
Sparsh Mittal181750.36