北京交通大学学报2017，Vol.41Issue(6)：34-41,8.DOI:10.11860/j.issn.1673-0291.2017.06.006
面向嵌入式应用的深度神经网络模型压缩技术综述
A survey on model compression of deep neural network for embedded system
摘要
Abstract
Combined the big data acquisition,the key technologies of deep neural network have widely applied in the field of image classification,object detection,speech recognition,natural language processing,et al.With the developing of the deep neural network model performance, the model size and the required calculation need to be improved,so that it is reliance on high power computing platform.This paper is focus on the deep neural network model compression technology for embedded applications in order to solve the problems of storage resource,memory access speed constraints and computing resources limit in embedded system.It aims to reduce the model size and the complex computation.Meanwhile,it could optimize the process of calculation. This paper has summarized the state-of-the-art model compression technologies including model pruning,fine model designing,tensor decomposition,model quantization,etc.Through the summary on the model development,it could provide the references for the studies of the deep neural network model compression technologies.关键词
深度神经网络/模型压缩/模型裁剪/张量分解/嵌入式系统Key words
deep neural network/model compression/model pruning/tensor decomposition/em-bedded system分类
信息技术与安全科学引用本文复制引用
王磊,赵英海,杨国顺,王若琪..面向嵌入式应用的深度神经网络模型压缩技术综述[J].北京交通大学学报,2017,41(6):34-41,8.基金项目
国家自然科学基金(61572065) National Natural Science Foundation of China (61572065) （61572065）
