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面向实时应用的深度学习研究综述
引用本文:张政馗,庞为光,谢文静,吕鸣松,王义.面向实时应用的深度学习研究综述[J].软件学报,2020,31(9).
作者姓名:张政馗  庞为光  谢文静  吕鸣松  王义
作者单位:东北大学 计算机科学与工程学院 智慧系统实验室, 辽宁 沈阳 110819,东北大学 计算机科学与工程学院 智慧系统实验室, 辽宁 沈阳 110819,东北大学 计算机科学与工程学院 智慧系统实验室, 辽宁 沈阳 110819,东北大学 计算机科学与工程学院 智慧系统实验室, 辽宁 沈阳 110819,东北大学 计算机科学与工程学院 智慧系统实验室, 辽宁 沈阳 110819
基金项目:国家自然科学基金(61532007,61772123);装备预研教育部联合基金青年人才基金(6141A020333)
摘    要:深度学习算法和GPU算力的不断进步正促进着人工智能技术在包括计算机视觉、语音识别、自然语言处理等领域得到广泛应用.与此同时,深度学习已经开始应用于以自动驾驶为代表的安全攸关领域.但是,近两年接连发生了几起严重的交通事故表明深度学习技术的成熟度还远未达到安全攸关应用的要求,因此对可信人工智能系统的研究已经成为了一个热点方向.本文对现有的面向实时应用的深度学习领域的研究工作进行了综述,首先介绍了深度学习技术应用于实时嵌入式系统所面临的关键设计问题,然后从深层神经网络的轻量化设计、GPU时间分析与任务调度、CPU+GPU SoC异构平台的资源管理、深层神经网络与网络加速器的协同设计等多个方面对现有的研究工作进行了分析和总结,最后展望了面向实时应用的深度学习领域进一步的研究方向.

关 键 词:深度学习  深层神经网络  实时系统  时间分析  实时调度  共享资源冲突
收稿时间:2019/7/8 0:00:00
修稿时间:2019/8/18 0:00:00

Deep Learning for Real-Time Applications: A Survey
Institution:Smart System Laboratory, School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China,Smart System Laboratory, School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China,Smart System Laboratory, School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China,Smart System Laboratory, School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China and Smart System Laboratory, School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Abstract:The persistent advance of deep learning algorithms and GPU computing power have promoted artificial intelligence in various fields including but not limited to compute vision, speech recognition, and natural language processing. Meaningwhile, deep learning already began exploiting its usage in safety-critical areas examplified by self-driving vehicles. Unfortunately, the successive severe traffic accidents in the past two years manifest that deep learning technology is still far from mature to fulfill safety-critical standards, and consequently the trustworthy artificial intelligence starts to attract a lot of research interests wordwide. This article conveys a state-of-the-art survey of the research on deep learning for real-time applications. It first introduces the main problems and challenges when deploying deep learning on the real-time embeded systems. Then, a detailed review covering various topics is provided, such as deep neural network lightweight design, GPU timing analysis and workload scheduling, shared resource management on the CPU+GPU SoC platform, deep neural network and network accelerator co-design. Finally, open issues and research directions are identified to conclude the survey.
Keywords:deep learninig  deep neural network  real-time systems  timing analysis  real-time scheduling  shared-resource interference
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