Ares: A framework for quantifying the resilience of deep neural networks

Brandon Reagen, Udit Gupta, Lillian Pentecost, Paul Whatmough, Sae Kyu Lee, Niamh Mulholland, David Brooks, Gu Yeon Wei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As the use of deep neural networks continues to grow, so does the fraction of compute cycles devoted to their execution. This has led the CAD and architecture communities to devote considerable attention to building DNN hardware. Despite these efforts, the fault tolerance of DNNs has generally been overlooked. This paper is the first to conduct a large-scale, empirical study of DNN resilience. Motivated by the inherent algorithmic resilience of DNNs, we are interested in understanding the relationship between fault rate and model accuracy. To do so, we present Ares: A light-weight, DNN-specific fault injection framework validated within 12% of real hardware. We find that DNN fault tolerance varies by orders of magnitude with respect to model, layer type, and structure.

Original languageEnglish (US)
Title of host publicationProceedings of the 55th Annual Design Automation Conference, DAC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781450357005
DOIs
StatePublished - Jun 24 2018
Event55th Annual Design Automation Conference, DAC 2018 - San Francisco, United States
Duration: Jun 24 2018Jun 29 2018

Publication series

NameProceedings - Design Automation Conference
VolumePart F137710
ISSN (Print)0738-100X

Other

Other55th Annual Design Automation Conference, DAC 2018
CountryUnited States
CitySan Francisco
Period6/24/186/29/18

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation

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