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Investigation on performance and energy efficiency of CNN-based object detection on embedded device

Title
Investigation on performance and energy efficiency of CNN-based object detection on embedded device
Authors
Oh S.Kim M.Kim D.Lee M.Jeong M.
Ewha Authors
이민수
SCOPUS Author ID
이민수scopus
Issue Date
2018
Journal Title
Proceedings of the 2017 4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017
Citation
Proceedings of the 2017 4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017 vol. 2018-January, pp. 1 - 4
Keywords
CNNembedded deviceenergy efficiencyobject detectionperformance
Publisher
Institute of Electrical and Electronics Engineers Inc.
Indexed
SCOPUS scopus
Document Type
Conference Paper
Abstract
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.
DOI
10.1109/CAIPT.2017.8320657
ISBN
9781538606001
Appears in Collections:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
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