TY - GEN
T1 - Embedded Frame Compression for Energy-Efficient Computer Vision Systems
AU - Guo, Li
AU - Zhou, Dajiang
AU - Zhou, Jinjia
AU - Kimura, Shinji
N1 - Funding Information:
This work is supported by Research Fellowships of Japan Society for the Promotion of Science for Young Scientists, by Waseda Univ. Graduate Program for Embodiment Informatics (FY2013-FY2019), and by State Key Laboratory of ASIC & Systems, Fudan University.
Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018/4/26
Y1 - 2018/4/26
N2 - 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.
AB - 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.
KW - computer vision
KW - embedded compression
KW - energy-efficient
KW - vision-oriented
UR - http://www.scopus.com/inward/record.url?scp=85051277735&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051277735&partnerID=8YFLogxK
U2 - 10.1109/ISCAS.2018.8351483
DO - 10.1109/ISCAS.2018.8351483
M3 - Conference contribution
AN - SCOPUS:85051277735
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
BT - 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018
Y2 - 27 May 2018 through 30 May 2018
ER -