Journal of Computer Science and Technology

, Volume 34, Issue 2, pp 305–317 | Cite as

Space Efficient Quantization for Deep Convolutional Neural Networks

  • Dong-Di Zhao
  • Fan LiEmail author
  • Kashif Sharif
  • Guang-Min Xia
  • Yu WangEmail author
Regular Paper


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.


convolutional neural network memory compression network quantization 


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Supplementary material

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Copyright information

© Springer Science+Business Media, LLC & Science Press, China 2019

Authors and Affiliations

  1. 1.School of Computer ScienceBeijing Institute of TechnologyBeijingChina
  2. 2.Wireless Networking and Sensing Laboratory, Department of Computer ScienceUniversity of North Carolina at CharlotteCharlotteU.S.A.

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