期刊论文

A Dynamic Quantization Coding Based Deep Neural Network Compression Method

Chen, J.-Y.

中文

自动化学报

0254-4156

2019

45

10

1960-1968

10.16383/j.aas.c180554

2018YFB1004504:国家重点研发计划 CCNU19Z02002:中央高校基本科研业务费专项 2018M632889:中国博士后科学基金 2017CFB504:湖北省自然科学基金 2017CFA007:湖北省创新研究团队 61702208:国家自然科学基金 61807014:国家自然科学基金

本校为第一且通讯机构

近年来深度神经网络(Deep neural network,DNN)从众多机器学习方法中脱颖而出,引起了广泛的兴趣和关注.然而,在主流的深度神经网络模型中,其参数数以百万计,需要消耗大量的计算和存储资源,难以应用于手机等移动嵌入式设备.为了解决这一问题,本文提出了一种基于动态量化编码(Dynamic quantization coding,DQC)的深度神经网络压缩方法.不同于现有的采用静态量化编码(Static quantitative coding,SQC)的方法,本文提出的方法在模型训练过程中同时对量化码本进行更新,使码本尽可能减小较大权重参数量化引起的误差.通过大量的对比实验表明,本文提出的方法优于现有基于静态编码的模型压缩方法.

Recently, deep neural network (DNN) stands out from many machine learning methods and has attracted wide interest and attention. However, it is difficult to apply DNN to mobile embedded devices such as mobile phones due to millions of parameters for the mainstream model of deep neural network, which requires a lot of calculation and storage resources. To address this problem, this paper proposes a deep neural network compressing method based on dynamic quantization coding (DQC). Different from the existing static quantitative coding (SQC) methods, the proposed method updates the quantized code...