TRQ: Ternary Neural Networks With Residual Quantization


  • Yue Li Beihang University
  • Wenrui Ding Beihang University
  • Chunlei Liu Beihang University
  • Baochang Zhang Beihang University
  • Guodong Guo Institute of Deep Learning,Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application


(Deep) Neural Network Algorithms, Classification and Regression, Applications


Ternary neural networks (TNNs) are potential for network acceleration by reducing the full-precision weights in network to ternary ones, e.g., {-1,0,1}. However, existing TNNs are mostly calculated based on rule-of-thumb quantization methods by simply thresholding operations, which causes a significant accuracy loss. In this paper, we introduce a stem-residual framework which provides new insight into Ternary quantization, termed Residual Quantization (TRQ), to achieve more powerful TNNs. Rather than directly thresholding operations, TRQ recursively performs quantization on full-precision weights for a refined reconstruction by combining the binarized stem and residual parts. With such a unique quantization process, TRQ endows the quantizer with high flexibility and precision. Our TRQ is generic, which can be easily extended to multiple bits through recursively encoded residual for a better recognition accuracy. Extensive experimental results demonstrate that the proposed method yields great recognition accuracy while being accelerated.




How to Cite

Li, Y., Ding, W., Liu, C., Zhang, B., & Guo, G. (2021). TRQ: Ternary Neural Networks With Residual Quantization. Proceedings of the AAAI Conference on Artificial Intelligence, 35(10), 8538-8546. Retrieved from



AAAI Technical Track on Machine Learning III