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Low-Power Computer Vision: Status, Challenges, Opportunities

15 April 2019
S. Alyamkin
M. Ardi
Alexander C. Berg
Achille Brighton
Bo Chen
Yiran Chen
Hsin-Pai Cheng
Zichen Fan
Chen Feng
Bo Fu
Kent W. Gauen
Abhinav Goel
A. Goncharenko
Xuyang Guo
Soonhoi Ha
Andrew G. Howard
Xiao Hu
Yuanjun Huang
Donghyun Kang
Jaeyoun Kim
Jong-gook Ko
A. Kondratyev
Junhyeok Lee
Seungjae Lee
S. W. Lee
Zichao Li
Zhiyu Liang
Juzheng Liu
Xin Liu
Yang Lu
Yung-Hsiang Lu
Deeptanshu Malik
Hong Hanh Nguyen
Eunbyung Park
D. Repin
Liang Shen
Tao Sheng
Fei Sun
D. Svitov
George K. Thiruvathukal
Baiwu Zhang
Jingchi Zhang
Xiaopeng Zhang
Shaojie Zhuo
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Abstract

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 article serves two main purposes: (1) 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 article summarizes 2018 winners' solutions. (2) Suggest directions for research as well as opportunities for low-power computer vision.

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