Low-Bit Quantization of Neural Network Based on Exponential Moving Average Knowledge Distillation
LÜ Junhuan1,2, XU Ke1,2, WANG Dong1,2
1. Institute of Information Science, Beijing Jiaotong University, Beijing 100044; 2. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing Jiaotong University, Beijing 100044
Abstract Now the memory and computational cost restrict the popularization of deep neural network application, whereas neural network quantization is an effective compression method. As the number of quantized bits is lower, the classification accuracy of neural networks becomes poorer in low-bit quantization of neural networks. To solve this problem, a low-bit quantization method of neural networks based on knowledge distillation is proposed. Firstly, a few images are exploited for adaptive initialization to train the quantization step of activation and weight to speed up the convergence of the quantization network. Then, the idea of exponential moving average knowledge distillation is introduced to normalize distillation loss and task loss and guide the training of quantization network. Experiments on ImageNet and CIFAR-10 datasets show that the performance of the proposed method is close to or better than that of the full precision network.
Fund:National Key Research and Development Program of China(No.2019YFB2204200), Fundamental Research Funds for the Central Universities(No.2020JBM020), Beijing Natural Science Foundation Program(No.4202063)
Corresponding Authors:
WANG Dong, Ph.D., professor. His research interests include image processing, model compression and chip design.
About author:: LÜ Junhuan, master student. Her research interests include neural network quantization and model compression. XU Ke, Ph.D. candidate. His research interests include neural network pruning, quan-tization and model compression.
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2021, Vol. 34 
