深度学习相关研究综述  

Review of deep learning

作　　者：张军阳 王慧丽[1] 郭阳[1] 扈啸[1] Zhang Junyang;Wang Huili;Guo Yang;Hu Xiao(Colloge of Computer,National University of Defense Technolgy,Changsha 410073,China)

机构地区：[1]国防科技大学计算机学院,长沙410073

出　　处：《计算机应用研究》2018年第7期1921-1928,1936,共9页Application Research of Computers

基　　金：国家自然科学基金资助项目(61572025);国家重点研发计划资助项目(2016YFB0200401)

摘　　要：为了能够及时跟踪深度学习技术的最新研究进展,把握深度学习技术当前的研究热点和方向,针对深度学习技术的相关研究内容进行综述。首先介绍了深度学习技术的应用背景、应用领域,指出研究深度学习技术的重要性,以及当前重要的几种神经网络模型及两种常用大规模模型训练并行方案,其目的在于从本质上理解深度学习的模型架构及其优化技巧。对比分析了当下主流的深度学习软件工具和相关的工业界研究平台,旨在为神经网络模型的实际使用提供借鉴;详细介绍了当下几种主流的深度学习硬件加速技术和最新研究现状,并对未来研究方向进行了展望。In order to keep track of the latest research progress of deep learning technology and grasp the current research hotspot and direction of deep learning,this paper reviewed the related research contents of deep learning technology. Firstly,it introduced the application background and application field of deep learning technology and pointed out the importance of studying on deep learning technology. Secondly,it introduced several important neural network models and two kinds of commonly used large-scale model training parallel scheme,which aimed to understand the deep learning model structure and its optimization skills. Then it analyzed the current mainstream learning tools and related industrial research platform,which aimed to provide reference for the practical use of neural network model. At the end,this paper introduced the hardware acceleration technology and the latest research status of several kinds of deep learning hardware acceleration in detail,and also discussed the future research directions.

关 键 词：深度学习 神经网络 算法模型 软件工具 硬件加速 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]