Embedded Frame Compression for Energy-Efficient Computer Vision Systems

Li Guo, Dajiang Zhou, Jinjia Zhou, Shinji Kimura

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781538648810
DOI
出版ステータスPublished - 2018 4 26
イベント2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Florence, Italy
継続期間: 2018 5 272018 5 30

出版物シリーズ

名前Proceedings - IEEE International Symposium on Circuits and Systems
2018-May
ISSN(印刷版)0271-4310

Other

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

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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