Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (2): 305-317.doi: 10.1007/s11390-019-1912-1

• Special Section of Advances in Computer Science and Technology—Current Advances in the NSFC Joint Research Fund for Overseas Chinese Scholars and Scholars in Hong Kong and Macao 2014-2017 (Part 2) • Previous Articles     Next Articles

Space Efficient Quantization for Deep Convolutional Neural Networks

Dong-Di Zhao1, Fan Li1,*, Member, CCF, ACM, IEEE, Kashif Sharif1, Member, CCF, ACM, IEEE, Guang-Min Xia1, Yu Wang2,*, Fellow, IEEE, Senior Member, ACM   

  1. 1 School of Computer Science, Beijing Institute of Technology, Beijing 100081, China;
    2 Wireless Networking and Sensing Laboratory, Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC 28223, U.S.A.
  • Received:2018-07-15 Revised:2019-01-27 Online:2019-03-05 Published:2019-03-16
  • Contact: Fan Li, Yu Wang E-mail:fli@bit.edu.cn;yu.wang@uncc.edu
  • About author:Dong-Di Zhao received his B.E. degree in the Internet of Things from the School of Computer Science, Beijing Institute of Technology, Beijing, in 2016. He is currently pursuing his Master's degree at Beijing Institute of Technology, Beijing. His research interests include mobile sensing, mobile computing, and deep learning.
  • Supported by:
    The work of Fan Li is partially supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 61772077 and 61370192, and Beijing Natural Science Foundation of China under Grant No. 4192051. The work of Yu Wang is partially supported by NSFC under Grant Nos. 61428203 and 61572347.

Deep convolutional neural networks (DCNNs) have shown outstanding performance in the fields of computer vision, natural language processing, and complex system analysis. With the improvement of performance with deeper layers, DCNNs incur higher computational complexity and larger storage requirement, making it extremely difficult to deploy DCNNs on resource-limited embedded systems (such as mobile devices or Internet of Things devices). Network quantization efficiently reduces storage space required by DCNNs. However, the performance of DCNNs often drops rapidly as the quantization bit reduces. In this article, we propose a space efficient quantization scheme which uses eight or less bits to represent the original 32-bit weights. We adopt singular value decomposition (SVD) method to decrease the parameter size of fully-connected layers for further compression. Additionally, we propose a weight clipping method based on dynamic boundary to improve the performance when using lower precision. Experimental results demonstrate that our approach can achieve up to approximately 14x compression while preserving almost the same accuracy compared with the full-precision models. The proposed weight clipping method can also significantly improve the performance of DCNNs when lower precision is required.

Key words: convolutional neural network, memory compression, network quantization

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