Investigation on performance and energy efficiency of CNN-based object detection on embedded device
- 주제(키워드) CNN , embedded device , energy efficiency , object detection , performance
- 지원기관 Ministry of Education
- 관리정보기술 faculty
- 등재 SCOPUS
- 발행기관 Institute of Electrical and Electronics Engineers Inc.
- 발행년도 2018
- URI http://www.dcollection.net/handler/ewha/000000155925
- ISBN 9781538606001
- 본문언어 영어
- Published As http://dx.doi.org/10.1109/CAIPT.2017.8320657
초록/요약
The use of a Convolutional Neural Network based method for object detection increases the accuracy that surpasses human visual system. Because it requires considerable computational capability, its use in embedded devices that place constraints in terms of power consumption as well as computational capability has thus far been limited. However, with the recent development of GPU for use in embedded devices and open-source software library for machine learning, it has become viable to utilize CNN in an energy-efficient embedded computing environment. In this study, CPU and GPU performance and energy efficiency of CNN-based object detection inference on an embedded platform is investigated through comparison with a traditional PC-based platform. Two publicly available hardware platforms are empirically evaluated; in one of them - NVIDIA Jetson TX-1 - the results demonstrate image processing performance of 65% of that of the PC, while the embedded device consumes 2.6% of power consumed by the PC. © 2017 IEEE.
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