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

November 24, 2020 · The Cartographer · 🏛 IEEE Journal on Miniaturization for Air and Space Systems

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"Title-pattern auto-detect: A Review of Recent Advances of Binary Neural Networks for Edge Computing"

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Authors Wenyu Zhao, Teli Ma, Xuan Gong, Baochang Zhang, David Doermann arXiv ID 2011.14824 Category cs.LG: Machine Learning Cross-listed cs.AI Citations 25 Venue IEEE Journal on Miniaturization for Air and Space Systems Last Checked 2 days ago

Abstract

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 paper reviews recent advances on binary neural network (BNN) and 1-bit CNN 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.