Low-Power Computer Vision: Status, Challenges, Opportunities
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.
Keywords
Cite
@article{arxiv.1904.07714,
title = {Low-Power Computer Vision: Status, Challenges, Opportunities},
author = {Sergei Alyamkin and Matthew Ardi and Alexander C. Berg and Achille Brighton and Bo Chen and Yiran Chen and Hsin-Pai Cheng and Zichen Fan and Chen Feng and Bo Fu and Kent Gauen and Abhinav Goel and Alexander Goncharenko and Xuyang Guo and Soonhoi Ha and Andrew Howard and Xiao Hu and Yuanjun Huang and Donghyun Kang and Jaeyoun Kim and Jong Gook Ko and Alexander Kondratyev and Junhyeok Lee and Seungjae Lee and Suwoong Lee and Zichao Li and Zhiyu Liang and Juzheng Liu and Xin Liu and Yang Lu and Yung-Hsiang Lu and Deeptanshu Malik and Hong Hanh Nguyen and Eunbyung Park and Denis Repin and Liang Shen and Tao Sheng and Fei Sun and David Svitov and George K. Thiruvathukal and Baiwu Zhang and Jingchi Zhang and Xiaopeng Zhang and Shaojie Zhuo},
journal= {arXiv preprint arXiv:1904.07714},
year = {2019}
}
Comments
Preprint, Accepted by IEEE Journal on Emerging and Selected Topics in Circuits and Systems. arXiv admin note: substantial text overlap with arXiv:1810.01732