Amphis: Managing Reconfigurable Processor Architectures With Generative Adversarial Learning

doi:10.1109/TCAD.2022.3197980

	Amphis: Managing Reconfigurable Processor Architectures With Generative Adversarial Learning
	Chen, Weiwei 1,2; Wang, Ying 1; Xu, Ying 1; Gao, Chengsi 1; Han, Yinhe 1; Zhang, Lei 1,2
	2022-11-01
发表期刊	IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
ISSN	0278-0070
卷号	41 期号:11 页码:3993-4003
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.
关键词	Resource management Predictive models Runtime Generators Generative adversarial networks Computational modeling Training Design space exploration generative adversarial network (GAN) reconfigurable processor
DOI	10.1109/TCAD.2022.3197980
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Interdisciplinary Applications ; Engineering, Electrical & Electronic
WOS记录号	WOS:000877295000040
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19839
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, Inst Comp Technol, SKLP, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, 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.