Embedded Frame Compression for Energy-Efficient Computer Vision Systems

Li Guo, Dajiang Zhou, Jinjia Zhou, Shinji Kimura

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

1 Citation (Scopus)

Abstract

Computer vision applications are rapidly gaining popularity in embedded systems, which typically involve a difficult trade-off between vision performance and energy consumption under a constraint of real-time processing throughput. Recently, hardware (FPGA and ASIC-based) implementations have emerged that significantly improve the energy efficiency of vision computation. These implementations, however, often involve intensive memory traffic that retains a significant portion of energy consumption at the system level. To address this issue, we present a lossy embedded compression framework to exploit the trade-off between vision performance and memory traffic for input images. Differential pulse-code modulation-based gradient-oriented quantization is developed as the lossy compression algorithm. We also present its hardware design that supports up to 12-scale 1080p@60fps real-time processing. For histogram of oriented gradient-based deformable part models on VOC2007, the proposed framework achieved a 49.6%-60.5% memory traffic reduction at a detection rate degradation of 0.05%-0.34%. For AlexNet on ImageNet, memory traffic reduction achieved up to 60.8% with less than 0.61% classification rate degradation.

Original languageEnglish
Title of host publication2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2018-May
ISBN (Electronic)9781538648810
DOIs
Publication statusPublished - 2018 Apr 26
Event2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Florence, Italy
Duration: 2018 May 272018 May 30

Other

Other2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018
CountryItaly
CityFlorence
Period18/5/2718/5/30

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Keywords

  • computer vision
  • embedded compression
  • energy-efficient
  • vision-oriented

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Guo, L., Zhou, D., Zhou, J., & Kimura, S. (2018). Embedded Frame Compression for Energy-Efficient Computer Vision Systems. In 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings (Vol. 2018-May). [8351483] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS.2018.8351483