CNQ: Compressor‐Based Non‐uniform Quantization of Deep Neural Networks

Chinese Journal of ElectronicsVolume 29, Issue 6 p. 1126-1133 ArticleFree Access CNQ: Compressor-Based Non-uniform Quantization of Deep Neural Networks Yong Yuan, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China University of Chinese Academy of Sciences, Beijing, 100049 ChinaSearch for more papers by this authorChen Chen, Corresponding Author chen.chen@ia.ac.cn Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China University of Chinese Academy of Sciences, Beijing, 100049 ChinaSearch for more papers by this authorXiyuan Hu, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094 ChinaSearch for more papers by this authorSilong Peng, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China University of Chinese Academy of Sciences, Beijing, 100049 China Beijing Visystem Co. Ltd, Beijing, 100083 ChinaSearch for more papers by this author Yong Yuan, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China University of Chinese Academy of Sciences, Beijing, 100049 ChinaSearch for more papers by this authorChen Chen, Corresponding Author chen.chen@ia.ac.cn Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China University of Chinese Academy of Sciences, Beijing, 100049 ChinaSearch for more papers by this authorXiyuan Hu, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094 ChinaSearch for more papers by this authorSilong Peng, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China University of Chinese Academy of Sciences, Beijing, 100049 China Beijing Visystem Co. Ltd, Beijing, 100083 ChinaSearch for more papers by this author First published: 01 November 2020 https://doi.org/10.1049/cje.2020.09.014AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract Deep neural networks (DNNs) have achieved state-of-the-art performance in a number of domains but suffer intensive complexity. Network quantization can effectively reduce computation and memory costs without changing network structure, facilitating the deployment of DNNs on mobile devices. While the existing methods can obtain good performance, low-bit quantization without time-consuming training or access to the full dataset is still a challenging problem. In this paper, we develop a novel method named Compressorbased non-uniform quantization (CNQ) method to achieve non-uniform quantization of DNNs with few unlabeled samples. Firstly, we present a compressor-based fast nonuniform quantization method, which can accomplish nonuniform quantization without iterations. Secondly, we propose to align the feature maps of the quantization model with the pre-trained model for accuracy recovery. Considering the property difference between different activation channels, we utilize the weighted-entropy perchannel to optimize the alignment loss. In the experiments, we evaluate the proposed method on image classification and object detection. Our results outperform the existing post-training quantization methods, which demonstrate the effectiveness of the proposed method. Volume29, Issue6November 2020Pages 1126-1133 RelatedInformation