Binary convolutional neural network: Review

DING Wenrui; LIU Chunlei; LI Yue; ZHANG Baochang

doi:10.7527/S1000-6893.2020.24504

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2021 , Vol. 42 >Issue 6: 24504 - 024504

DOI: https://doi.org/10.7527/S1000-6893.2020.24504

Review

Binary convolutional neural network: Review

DING Wenrui ,
LIU Chunlei ,
LI Yue ,
ZHANG Baochang

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1. Unmanned System Research Institute, Beihang University, Beijing 100083, China;
2. School of Electronic and Information Engineering, Beihang University, Beijing 100083, China;
3. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China

Received date: 2020-07-07

Revised date: 2020-08-03

Online published: 2020-09-24

Supported by

National Natural Science Foundation of China (U20B2042); National Natural Science Foundation of China (62076019); Science and Technology Innovation 2030-Key Project of "New Generation Artificial Intelligence" (2020AAA0108200)

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Abstract

In recent years, Binary Convolutional Neural Networks (BNNs) have attracted much attention owing to their low storage and high computational efficiency. However, the mismatch between forward and backward quantization results in a huge performance gap between the BNN and the full-precision convolutional neural network, affecting the deployment of the BNN on resource-constrained platforms. Researchers have proposed a series of algorithms and training methods to reduce the performance gap during the binarization process, thereby promoting the application of BNNs to embedded portable devices. This paper makes a comprehensive review of BNNs, mainly from the perspectives of improving network representative capabilities and fully exploring the network training potential. Specifically, improving network representative capabilities includes the design of the binary quantization method and structure design, while fully exploring the network training potential involves loss function design and the training strategy. Finally, we discuss the performance of BNNs in different tasks and hardware platforms, and summarize the challenges in future research.

Key words： binary convolutional neural networks; full-precision convolutional neural networks; binarization; quantization; model compression; lightweight; deep learning

Cite this article

DING Wenrui , LIU Chunlei , LI Yue , ZHANG Baochang . Binary convolutional neural network: Review[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021 , 42(6) : 24504 -024504 . DOI: 10.7527/S1000-6893.2020.24504

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