EVA2: Exploiting temporal redundancy in live computer vision

Mark Buckler, Philip Bedoukian, Suren Jayasuriya, Adrian Sampson

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

9 Scopus citations

Abstract

Hardware support for deep convolutional neural networks (CNNs) is critical to advanced computer vision in mobile and embedded devices. Current designs, however, accelerate generic CNNs; they do not exploit the unique characteristics of real-time vision. We propose to use the temporal redundancy in natural video to avoid unnecessary computation on most frames. A new algorithm, activation motion compensation, detects changes in the visual input and incrementally updates a previously-computed activation. The technique takes inspiration from video compression and applies well-known motion estimation techniques to adapt to visual changes. We use an adaptive key frame rate to control the trade-off between efficiency and vision quality as the input changes. We implement the technique in hardware as an extension to state-of-the-art CNN accelerator designs. The new unit reduces the average energy per frame by 54%, 62%, and 87% for three CNNs with less than 1% loss in vision accuracy.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture, ISCA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages533-546
Number of pages14
ISBN (Electronic)9781538659847
DOIs
StatePublished - Jul 19 2018
Event45th ACM/IEEE Annual International Symposium on Computer Architecture, ISCA 2018 - Los Angeles, United States
Duration: Jun 2 2018Jun 6 2018

Publication series

NameProceedings - International Symposium on Computer Architecture
ISSN (Print)1063-6897

Other

Other45th ACM/IEEE Annual International Symposium on Computer Architecture, ISCA 2018
CountryUnited States
CityLos Angeles
Period6/2/186/6/18

Keywords

  • Application specific integrated circuits
  • Computer architecture
  • Computer vision
  • Convolutional neural networks
  • Hardware acceleration
  • Video compression

ASJC Scopus subject areas

  • Hardware and Architecture

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    Buckler, M., Bedoukian, P., Jayasuriya, S., & Sampson, A. (2018). EVA2: Exploiting temporal redundancy in live computer vision. In Proceedings - 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture, ISCA 2018 (pp. 533-546). [8416853] (Proceedings - International Symposium on Computer Architecture). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCA.2018.00051