View : 69 Download: 0
Investigation on performance and energy efficiency of CNN-based object detection on embedded device
- Investigation on performance and energy efficiency of CNN-based object detection on embedded device
- Oh S.; Kim M.; Kim D.; Lee M.; Jeong M.
- Ewha Authors
- SCOPUS Author ID
- Issue Date
- Journal Title
- Proceedings of the 2017 4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017
- Proceedings of the 2017 4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017 vol. 2018-January, pp. 1 - 4
- CNN; embedded device; energy efficiency; object detection; performance
- Institute of Electrical and Electronics Engineers Inc.
- Document Type
- Conference Paper
- 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.
- Appears in Collections:
- 엘텍공과대학 > 컴퓨터공학과 > Journal papers
- Files in This Item:
There are no files associated with this item.
- RIS (EndNote)
- XLS (Excel)
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.