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MV-Net: Toward Real-Time Deep Learning on Mobile GPGPU Systems

文献类型：期刊论文

作者Tang, Yibin2,3; Wang, Ying2,3; Li, Huawei1,2,3; Li, Xiaowei2,3,4
刊名ACM JOURNAL ON EMERGING TECHNOLOGIES IN COMPUTING SYSTEMS
出版日期2019-12-01
卷号15期号:4页码:25
关键词Edge computing online scheduling deep learning energy efficiency approximate computing
ISSN号1550-4832
DOI10.1145/3358696
英文摘要Recently the development of deep learning has been propelling the sheer growth of vision and speech applications on lightweight embedded and mobile systems. However, the limitation of computation resource and power delivery capability in embedded platforms is recognized as a significant bottleneck that prevents the systems from providing real-time deep learning ability, since the inference of deep convolutional neural networks (CNNs) and recurrent neural networks (RNNs) involves large quantities of weights and operations. Particularly, how to provide quality-of-services (QoS)-guaranteed neural network inference ability in the multitask execution environment of multicore SoCs is even more complicated due to the existence of resource contention. In this article, we present a novel deep neural network architecture, MV-Net, which provides performance elasticity and contention-aware self-scheduling ability for QoS enhancement in mobile computing systems. When the constraints of QoS, output accuracy, and resource contention status of the system change, MV-Net can dynamically reconfigure the corresponding neural network propagation paths and thus achieves an effective tradeoff between neural network computational complexity and prediction accuracy via approximate computing. The experimental results show that (1) MV-Net significantly improves the performance flexibility of current CNN models and makes it possible to provide always-guaranteed QoS in a multitask environment, and (2) it satisfies the quality-of-results (QoR) requirement, outperforming the baseline implementation significantly, and improves the system energy efficiency at the same time.
资助项目National Natural Science Foundation of China[61874124] ; National Natural Science Foundation of China[61876173] ; National Natural Science Foundation of China[61432017] ; National Natural Science Foundation of China[61532017] ; National Natural Science Foundation of China[YESS2016qnrc001]
WOS研究方向Computer Science ; Engineering ; Science & Technology - Other Topics
语种英语
WOS记录号WOS:000535716700005
出版者ASSOC COMPUTING MACHINERY
源URL[http://119.78.100.204/handle/2XEOYT63/15288]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Tang, Yibin; Li, Huawei
作者单位1.Peng Cheng Lab, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.6 Ke Xue Yuan South Rd, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Tang, Yibin,Wang, Ying,Li, Huawei,et al. MV-Net: Toward Real-Time Deep Learning on Mobile GPGPU Systems[J]. ACM JOURNAL ON EMERGING TECHNOLOGIES IN COMPUTING SYSTEMS,2019,15(4):25.
APA Tang, Yibin,Wang, Ying,Li, Huawei,&Li, Xiaowei.(2019).MV-Net: Toward Real-Time Deep Learning on Mobile GPGPU Systems.ACM JOURNAL ON EMERGING TECHNOLOGIES IN COMPUTING SYSTEMS,15(4),25.
MLA Tang, Yibin,et al."MV-Net: Toward Real-Time Deep Learning on Mobile GPGPU Systems".ACM JOURNAL ON EMERGING TECHNOLOGIES IN COMPUTING SYSTEMS 15.4(2019):25.

入库方式： OAI收割

来源：计算技术研究所

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