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A QoS-QoR Aware CNN Accelerator Design Approach

文献类型：期刊论文

作者Wang, Ying1,2; Li, Huawei1,2; Cheng, Long3; Li, Xiaowei1,2
刊名IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
出版日期2019-11-01
卷号38期号:11页码:1995-2007
关键词Approximate computing convolutional neural network (CNN) deep learning (DL) quality of service (QoS) real-time
ISSN号0278-0070
DOI10.1109/TCAD.2018.2877010
英文摘要Recently powerful convolutional neural network (CNN) accelerators are emerging as energy-efficient solutions for real-time vision/speech processing, recognition and a wide spectrum of approximate computing applications. In addition to the broad applicability scope of such deep learning (DL) accelerators, we found that the fascinating feature of deterministic performance makes them ideal candidates as application-processors in embedded SoCs concerned with real-time processing. However, unlike traditional accelerator designs, DL accelerators introduce the new aspect of design tradeoff between real-time processing [quality of service (QoS)] and computation approximation [quality of result (QoR)] into embedded systems. This paper proposes an elastic CNN acceleration architecture that automatically adapts to the user-specified QoS constraint by exploiting the error-resilience in typical approximate computing workloads. For the first time, the proposed design, including the network tuning-and-mapping software and reconfigurable accelerator hardware, aims to reconcile the design constraint of QoS and QoR, which are respectively, the critical concerns in real-time and approximate computing. It is shown in experiments the proposed architecture enables the embedded system to work flexibly in an expanded operating space, significantly enhances its real-time ability, and maximizes the system energy-efficiency within the user-specified QoS-QoR constraint through self-reconfiguration. Also, we showcase the application of the proposed design approach to lower power image recognition challenge (LPIRC) and how it is employed to forge an energy-efficient solution to the LPIRC contest.
资助项目National Natural Science Foundation of China[61874124] ; National Natural Science Foundation of China[61504153] ; National Natural Science Foundation of China[61432017] ; National Natural Science Foundation of China[61532017] ; National Natural Science Foundation of China[61402146] ; National Natural Science Foundation of China[61521092] ; YESS Hip Program by CAST
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000505522900001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/14986]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Li, Huawei; Li, Xiaowei
作者单位1.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China
3.Univ Coll Dublin, Dublin D04 V1W8, Ireland
推荐引用方式
GB/T 7714		 Wang, Ying,Li, Huawei,Cheng, Long,et al. A QoS-QoR Aware CNN Accelerator Design Approach[J]. IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,2019,38(11):1995-2007.
APA Wang, Ying,Li, Huawei,Cheng, Long,&Li, Xiaowei.(2019).A QoS-QoR Aware CNN Accelerator Design Approach.IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,38(11),1995-2007.
MLA Wang, Ying,et al."A QoS-QoR Aware CNN Accelerator Design Approach".IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 38.11(2019):1995-2007.

入库方式： OAI收割

来源：计算技术研究所

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