A Review of Recent Advances of Binary Neural Networks for Edge Computing

被引：12
|
作者：
Zhao W. [1 ]
Ma T. [1 ]
Gong X. [2 ]
Zhang B. [1 ]
Doermann D. [2 ]
机构：
[1] School of Automation Science and Electrical Engineering, Beihang University, Beijing
[2] Department of Computer Science and Engineering, University at Buffalo, Buffalo, 14260, NY
来源：
IEEE Journal on Miniaturization for Air and Space Systems | 2021年 / 2卷 / 01期
基金：
中国国家自然科学基金;
关键词：
1-bit convolutional neural network (CNN); binary neural network (BNN); edge computing; front-end computing; neural architecture search;
D O I：
10.1109/JMASS.2020.3034205
中图分类号：
学科分类号：
摘要：
Edge computing is promising to become one of the next hottest topics in artificial intelligence because it benefits various evolving domains, such as real-time unmanned aerial systems, industrial applications, and the demand for privacy protection. This article reviews the recent advances on binary neural network (BNN) and 1-bit convolutional neural network technologies that are well suitable for front-end, edge-based computing. We introduce and summarize existing work and classify them based on gradient approximation, quantization, architecture, loss functions, optimization method, and binary neural architecture search. We also introduce applications in the areas of computer vision and speech recognition and discuss future applications for edge computing. © 2019 IEEE.
引用
收藏
页码：25 / 35
页数：10
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