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Amphis: Managing Reconfigurable Processor Architectures With Generative Adversarial Learning

文献类型：期刊论文

作者Chen, Weiwei1,2; Wang, Ying2; Xu, Ying2; Gao, Chengsi2; Han, Yinhe2; Zhang, Lei1,2
刊名IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
出版日期2022-11-01
卷号41期号:11页码:3993-4003
关键词Resource management Predictive models Runtime Generators Generative adversarial networks Computational modeling Training Design space exploration generative adversarial network (GAN) reconfigurable processor
ISSN号0278-0070
DOI10.1109/TCAD.2022.3197980
英文摘要Dynamic resources management in reconfigurable processors often manifests as a hard online decision-making task, which should yield premier solutions that must meet Quality-of-Service (QoS) requirements while maximizing the system's efficiency. Most prior works rely on a hard-to-train predictor to model the complicated relationships between processor configurations and performance. To decide the proper resource allocation, the predictor needs to tentatively evaluate a group of possible configurations, and then decide the best configuration for the workload. This tedious process has an expensive runtime overhead for resource configuration in processors. Besides, prior works focus on improving the prediction accuracy, however, higher performance prediction cannot guarantee a good system outcome. Inspired by recent advances in adversarial learning, we present a generative adversarial network (GAN)-based framework, Amphis, which can directly generate the on-demand processor configuration for any scheduled-in application. By evaluating Amphis on a reconfigurable processor with 18 different workloads, our results demonstrate that the GAN-based method provides tremendous overhead reduction (up to 90%) compared to the SOTA prediction-based method WNNM while providing higher resource utilization.
资助项目National Natural Science Foundation of China[62090024] ; National Natural Science Foundation of China[61876173]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000877295000040
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/19838]  
专题中国科学院计算技术研究所期刊论文
通讯作者Wang, Ying
作者单位1.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, SKLP, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Weiwei,Wang, Ying,Xu, Ying,et al. Amphis: Managing Reconfigurable Processor Architectures With Generative Adversarial Learning[J]. IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,2022,41(11):3993-4003.
APA Chen, Weiwei,Wang, Ying,Xu, Ying,Gao, Chengsi,Han, Yinhe,&Zhang, Lei.(2022).Amphis: Managing Reconfigurable Processor Architectures With Generative Adversarial Learning.IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,41(11),3993-4003.
MLA Chen, Weiwei,et al."Amphis: Managing Reconfigurable Processor Architectures With Generative Adversarial Learning".IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 41.11(2022):3993-4003.

入库方式： OAI收割

来源：计算技术研究所

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