A power-efficient hybrid architecture design for image recognition using CNNs

Jinhang Choi, Srivatsa Srinivasa, Yasuki Tanabe, Jack Sampson, Vijaykrishnan Narayanan

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

Abstract

Convolutional Neural Networks (CNNs) are proving to be highly effective in vision recognition systems. However, it is a challenge to use them in real-Time embedded systems because of their requirements for computation-intensive operations and high memory bandwidth. This paper proposes a power-efficient CNN architecture that has a pipelined streaming accelerator coupled to 4,096 SIMD Processing Elements. We reduce memory bandwidth via hierarchical intermediate data buffering and batch processing on the chip. As a result, we achieve high power-efficiency: Our proposed design processes 2,175 regions/second when operating at 500MHz with a power budget less than 7.5 Watts.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2018
PublisherIEEE Computer Society
Pages22-27
Number of pages6
ISBN (Print)9781538670996
DOIs
StatePublished - Aug 7 2018
Event17th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2018 - Hong Kong, Hong Kong
Duration: Jul 9 2018Jul 11 2018

Publication series

NameProceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI
Volume2018-July
ISSN (Print)2159-3469
ISSN (Electronic)2159-3477

Other

Other17th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2018
CountryHong Kong
CityHong Kong
Period7/9/187/11/18

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Choi, J., Srinivasa, S., Tanabe, Y., Sampson, J., & Narayanan, V. (2018). A power-efficient hybrid architecture design for image recognition using CNNs. In Proceedings - 2018 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2018 (pp. 22-27). [8429336] (Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI; Vol. 2018-July). IEEE Computer Society. https://doi.org/10.1109/ISVLSI.2018.00015