| Autor: |
Han Cai, Wei-Xiang Guo, Yuwei Chen, Rick Lee, Guiguang Ding, Yubei Chen, Yuan-Yao Sung, YoungMin Kwon, Shengwen Liang, Seonghwan Jeong, Zhenyu Wu, Kai-Chiang Wu, Zerun Wang, Song Han, Xiangru Lian, Tianzhe Wang, Ming-Ching Chang, Xiao Hu, Jiayi Shen, Jianchao Tan, Zhangyang Wang, Pengcheng Pi, Zhenyu Hu, Ligeng Zhu, Zhekai Zhang, Gang Zhang, Yu-Shin Han, Ji Liu, Rahul Sridhar, Teng Xi, Yunhe Xue, Jeffery Pan, Chia-Hsiang Liu, Yi Lee |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
AICAS |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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