2018 Low-Power Image Recognition Challenge
S. Alyamkin
M. Ardi
Achille Brighton
Alexander C. Berg
Yiran Chen
Hsin-Pai Cheng
Bo Chen
Zichen Fan
Chen Feng
Bo Fu
Kent W. Gauen
Jongkook Go
A. Goncharenko
Xuyang Guo
Hong Hanh Nguyen
Andrew G. Howard
Yuanjun Huang
Donghyun Kang
Jaeyoun Kim
A. Kondratyev
Seungjae Lee
Suwoong Lee
Junhyeok Lee
Zhiyu Liang
Xin Liu
Juzheng Liu
Zi-mei Li
Yang Lu
Yung-Hsiang Lu
Deeptanshu Malik
Eunbyung Park
D. Repin
Tao Sheng
Liang Shen
Fei Sun
D. Svitov
George K. Thiruvathukal
Baiwu Zhang
Jingchi Zhang
Xiaopeng Zhang
Shaojie Zhuo

Abstract
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing.ieee.org/lpirc) is an annual competition started in 2015. The competition identifies the best technologies that can classify and detect objects in images efficiently (short execution time and low energy consumption) and accurately (high precision). Over the four years, the winners' scores have improved more than 24 times. As computer vision is widely used in many battery-powered systems (such as drones and mobile phones), the need for low-power computer vision will become increasingly important. This paper summarizes LPIRC 2018 by describing the three different tracks and the winners' solutions.
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