Deep Learning for Spatial Supply Noise Estimation in a Processor Chip

doi:10.1109/ICSICT.2018.8565784

	Deep Learning for Spatial Supply Noise Estimation in a Processor Chip
	Weng, Rusong; Li, Yaguang; Gao, Wei; Wang, Leilei; Kou, Xufeng; Zhou, Pingqiang
	2018
会议录名称	2018 14TH IEEE INTERNATIONAL CONFERENCE ON SOLID-STATE AND INTEGRATED CIRCUIT TECHNOLOGY (ICSICT)
页码	1379-1382
发表状态	已发表
DOI	10.1109/ICSICT.2018.8565784
摘要	To detect voltage emergency caused by supply voltage fluctuation, people have proposed linear models to estimate the supply noise at a hotspot based on the readings of limited on-chip noise sensors. But such linear model only works well in a power delivery network with linear elements. In this work, we propose to use deep learning to build an accurate noise estimation model for each hotspot in a real chip with nonlinear circuit loads. In our experiments we test the performance of our deep learning based approach on a set of benchmarks with different degree of non-linearity, and we also compare its performance with prior work.
关键词	Load modeling Integrated circuit modeling Estimation Sensors Benchmark testing Transistors
出版地	345 E 47TH ST, NEW YORK, NY 10017 USA
会议地点	Qingdao
会议日期	31 Oct.-3 Nov. 2018
URL	查看原文
收录类别	CPCI ; CPCI-S ; EI
语种	英语
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000458919700412
出版者	IEEE
EI入藏号	20190406415341
EI主题词	Benchmarking ; Electric power transmission ; Integrated circuits
EI分类号	Electric Power Transmission:706.1.1 ; Semiconductor Devices and Integrated Circuits:714.2
原始文献类型	Proceedings Paper
来源库	IEEE
引用统计	正在获取...
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/29579
专题	信息科学与技术学院_博士生信息科学与技术学院_PI研究组_寇煦丰组信息科学与技术学院_PI研究组_周平强组信息科学与技术学院_硕士生信息科学与技术学院_本科生
通讯作者	Weng, Rusong
作者单位	ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China
第一作者单位	信息科学与技术学院
通讯作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Weng, Rusong,Li, Yaguang,Gao, Wei,et al. Deep Learning for Spatial Supply Noise Estimation in a Processor Chip[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2018:1379-1382.