RedEye: Analog ConvNet Image Sensor Architecture for Continuous Mobile Vision

Robert LiKamWa, Yunhui Hou, Yuan Gao, Mia Polansky, Lin Zhong

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

105 Scopus citations

Abstract

Continuous mobile vision is limited by the inability to efficiently capture image frames and process vision features. This is largely due to the energy burden of analog readout circuitry, data traffic, and intensive computation. To promote efficiency, we shift early vision processing into the analog domain. This results in RedEye, an analog convolutional image sensor that performs layers of a convolutional neural network in the analog domain before quantization. We design RedEye to mitigate analog design complexity, using a modular column-parallel design to promote physical design reuse and algorithmic cyclic reuse. RedEye uses programmable mechanisms to admit noise for tunable energy reduction. Compared to conventional systems, RedEye reports an 85% reduction in sensor energy, 73% reduction in cloudlet-based system energy, and a 45% reduction in computation-based system energy.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 43rd International Symposium on Computer Architecture, ISCA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages255-266
Number of pages12
ISBN (Electronic)9781467389471
DOIs
StatePublished - Aug 24 2016
Externally publishedYes
Event43rd International Symposium on Computer Architecture, ISCA 2016 - Seoul, Korea, Republic of
Duration: Jun 18 2016Jun 22 2016

Other

Other43rd International Symposium on Computer Architecture, ISCA 2016
CountryKorea, Republic of
CitySeoul
Period6/18/166/22/16

Keywords

  • computer vision
  • continuous mobile vision
  • pre-quantization processing
  • programmable analog computing

ASJC Scopus subject areas

  • Hardware and Architecture

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