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The 2020 Low-Power Computer Vision Challenge

Ming‐Ching Chang, Yuwei Chen, Rahul Sridhar, Zhenyu Hu, Yunhe Xue, Zhenyu Wu, Pengcheng Pi, Jiayi Shen, Jianchao Tan, Xiangru Lian, Ji Liu, Zhangyang Wang, Chia-Hsiang Liu, Yu-Shin Han, Yuan-Yao Sung, Yi Lee, Kai–Chiang Wu, Weixiang Guo, Shengwen Liang, Zerun Wang

Year
2021
Citations
3

Abstract

AI computer vision has advanced significantly in recent years. IoT and edge computing devices such as mobile phones have become the primary computing platform for many end users. Mobile devices such as robots and drones that rely on batteries demand for energy efficient computation. Since 2015, the IEEE Annual International Low-Power Computer Vision Challenge (LPCVC) was held to identify energy-efficient AI and computer vision solutions. The 2020 LPCVC includes three challenge tracks: (1) PyTorch UAV Video Track, (2) FPGA Image Track, and (3) On-device Visual Intelligence Competition (OVIC) Tenforflow Track. This paper summarizes the 2020 winning solutions from the three tracks of LPCVC competitions. Methods and future directions for energy-efficient AI and computer vision research are discussed.

Keywords

Computer scienceTrack (disk drive)Mobile deviceArtificial intelligenceComputationEdge computingMobile robotDroneEfficient energy useRobot

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