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Low-Power Computer Vision: Status, Challenges, and Opportunities

Publication ,  Journal Article
Alyamkin, S; Ardi, M; Berg, AC; Brighton, A; Chen, B; Chen, Y; Cheng, HP; Fan, Z; Feng, C; Fu, B; Gauen, K; Goel, A; Goncharenko, A; Guo, X ...
Published in: IEEE Journal on Emerging and Selected Topics in Circuits and Systems
June 1, 2019
Published version (DOI)

Computer vision has achieved impressive progress in recent years. Meanwhile, mobile phones have become the primary computing platforms for millions of people. In addition to mobile phones, many autonomous systems rely on visual data for making decisions, and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots). These systems rely on batteries, and energy efficiency is critical. This paper serves the following two main purposes. First, examine the state of the art for low-power solutions to detect objects in images. Since 2015, the IEEE Annual International Low-Power Image Recognition Challenge (LPIRC) has been held to identify the most energy-efficient computer vision solutions. This paper summarizes the 2018 winners' solutions. Second, suggest directions for research as well as opportunities for low-power computer vision.

Duke Scholars

Yiran Chen
Author Yiran Chen Electrical and Computer Engineering
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Published In

IEEE Journal on Emerging and Selected Topics in Circuits and Systems

DOI

10.1109/JETCAS.2019.2911899

EISSN

2156-3365

ISSN

2156-3357

Publication Date

June 1, 2019

Volume

9

Issue

2

Start / End Page

411 / 421

Related Subject Headings

  • 4008 Electrical engineering
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Alyamkin, S., Ardi, M., Berg, A. C., Brighton, A., Chen, B., Chen, Y., … Zhuo, S. (2019). Low-Power Computer Vision: Status, Challenges, and Opportunities. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 9(2), 411–421. https://doi.org/10.1109/JETCAS.2019.2911899
Alyamkin, S., M. Ardi, A. C. Berg, A. Brighton, B. Chen, Y. Chen, H. P. Cheng, et al. “Low-Power Computer Vision: Status, Challenges, and Opportunities.” IEEE Journal on Emerging and Selected Topics in Circuits and Systems 9, no. 2 (June 1, 2019): 411–21. https://doi.org/10.1109/JETCAS.2019.2911899.
Alyamkin S, Ardi M, Berg AC, Brighton A, Chen B, Chen Y, et al. Low-Power Computer Vision: Status, Challenges, and Opportunities. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 2019 Jun 1;9(2):411–21.
Alyamkin, S., et al. “Low-Power Computer Vision: Status, Challenges, and Opportunities.” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 9, no. 2, June 2019, pp. 411–21. Scopus, doi:10.1109/JETCAS.2019.2911899.
Alyamkin S, Ardi M, Berg AC, Brighton A, Chen B, Chen Y, Cheng HP, Fan Z, Feng C, Fu B, Gauen K, Goel A, Goncharenko A, Guo X, Ha S, Howard A, Hu X, Huang Y, Kang D, Kim J, Ko JG, Kondratyev A, Lee J, Lee S, Li Z, Liang Z, Liu J, Liu X, Lu Y, Lu YH, Malik D, Nguyen HH, Park E, Repin D, Shen L, Sheng T, Sun F, Svitov D, Thiruvathukal GK, Zhang B, Zhang J, Zhang X, Zhuo S. Low-Power Computer Vision: Status, Challenges, and Opportunities. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 2019 Jun 1;9(2):411–421.

Published In

IEEE Journal on Emerging and Selected Topics in Circuits and Systems

DOI

10.1109/JETCAS.2019.2911899

EISSN

2156-3365

ISSN

2156-3357

Publication Date

June 1, 2019

Volume

9

Issue

2

Start / End Page

411 / 421

Related Subject Headings

  • 4008 Electrical engineering