2018 Low-Power Image Recognition Challenge
Sergei Alyamkin, Matthew Ardi, Achille Brighton, Alexander C. Berg,, Yiran Chen, Hsin-Pai Cheng, Bo Chen, Zichen Fan, Chen Feng, Bo Fu, Kent, Gauen, Jongkook Go, Alexander Goncharenko, Xuyang Guo, Hong Hanh Nguyen,, Andrew Howard, Yuanjun Huang, Donghyun Kang, Jaeyoun Kim
TL;DR
The 2018 Low-Power Image Recognition Challenge evaluates the latest low-power, high-accuracy image classification and detection technologies across multiple tracks, highlighting significant improvements and innovative solutions for energy-efficient computer vision.
Contribution
This paper summarizes the 2018 LPIRC, detailing the competition's tracks, winners' solutions, and recent advancements in low-power, high-accuracy image recognition technologies.
Findings
Scores improved over 24 times since inception
Multiple innovative low-power solutions were proposed
Enhanced efficiency in battery-powered vision systems
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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Taxonomy
TopicsCOVID-19 diagnosis using AI
MethodsRectified Linear Unit HAHA