1. Low-Power Computer Vision: Status, Challenges, Opportunities
- Author
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Alyamkin, Sergei, Ardi, Matthew, Berg, Alexander C., Brighton, Achille, Chen, Bo, Chen, Yiran, Cheng, Hsin-Pai, Fan, Zichen, Feng, Chen, Fu, Bo, Gauen, Kent, Goel, Abhinav, Goncharenko, Alexander, Guo, Xuyang, Ha, Soonhoi, Howard, Andrew, Hu, Xiao, Huang, Yuanjun, Kang, Donghyun, Kim, Jaeyoun, Ko, Jong Gook, Kondratyev, Alexander, Lee, Junhyeok, Lee, Seungjae, Lee, Suwoong, Li, Zichao, Liang, Zhiyu, Liu, Juzheng, Liu, Xin, Lu, Yang, Lu, Yung-Hsiang, Malik, Deeptanshu, Nguyen, Hong Hanh, Park, Eunbyung, Repin, Denis, Shen, Liang, Sheng, Tao, Sun, Fei, Svitov, David, Thiruvathukal, George K., Zhang, Baiwu, Zhang, Jingchi, Zhang, Xiaopeng, and Zhuo, Shaojie
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Computer Science - Performance - 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., Comment: Preprint, Accepted by IEEE Journal on Emerging and Selected Topics in Circuits and Systems. arXiv admin note: substantial text overlap with arXiv:1810.01732
- Published
- 2019