Sensitivity based error resilient techniques for energy efficient deep neural network accelerators

Wonseok Choi, Dongyeob Shin, Jongsun Park, Swaroop Ghosh

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

5 Citations (Scopus)

Abstract

With inherent algorithmic error resilience of deep neural networks (DNNs), supply voltage scaling could be a promising technique for energy efficient DNN accelerator design. In this paper, we propose novel error resilient techniques to enable aggressive voltage scaling by exploiting different amount of error resilience (sensitivity) with respect to DNN layers, filters, and channels. First, to rapidly evaluate filter/channel-level weight sensitivities of large scale DNNs, first-order Taylor expansion is used, which accurately approximates weight sensitivity from actual error injection simulation. With measured timing error probability of each multiply-accumulate (MAC) units considering process variations, the sensitivity variation among filter weights can be leveraged to design DNN accelerator, such that the computations with more sensitive weights are assigned to more robust MAC units, while those with less sensitive weights are assigned to less robust MAC units. Based on post-synthesis timing simulations, 51% energy savings has been achieved with CIFAR-10 dataset using VGG-9 compared to state-of-the-art timing error recovery technique with the same constraint of 3% accuracy loss.

Original languageEnglish
Title of host publicationProceedings of the 56th Annual Design Automation Conference 2019, DAC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450367257
DOIs
Publication statusPublished - 2019 Jun 2
Event56th Annual Design Automation Conference, DAC 2019 - Las Vegas, United States
Duration: 2019 Jun 22019 Jun 6

Publication series

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

Conference

Conference56th Annual Design Automation Conference, DAC 2019
CountryUnited States
CityLas Vegas
Period19/6/219/6/6

ASJC Scopus subject areas

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

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  • Cite this

    Choi, W., Shin, D., Park, J., & Ghosh, S. (2019). Sensitivity based error resilient techniques for energy efficient deep neural network accelerators. In Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019 [a204] (Proceedings - Design Automation Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1145/3316781.3317908