NDLI: Efficient smart sampling based full-chip leakage analysis for intra-die variation considering state dependence

Content Provider	IEEE Xplore Digital Library
Author	Veetil, V. Sylvester, D. Blaauw, D. Shah, S. Rochel, S.
Copyright Year	2009
Description	Author affiliation: EECS Dept., Univ. of Michigan, Ann Arbor, MI, USA (Veetil, V.; Sylvester, D.; Blaauw, D.)
Abstract	Leakage power minimization is critical to semiconductor design in nanoscale CMOS. On the other hand increasing variability with scaling adds complexity to the leakage analysis problem. In this work we seek to achieve tractability in Monte Carlo-based statistical leakage analysis. A novel approach for fast and accurate statistical leakage analysis considering inter-die and intra-die components is proposed. We show that the optimal way to select samples, to capture intra-die variation accurately, is according to the probability distribution function of total process variation. Intelligent selection of samples is performed using a Quasi Monte Carlo technique. Results are presented for benchmarks with sizes varying from approximately 5,000 to 200,000 gates. The largest benchmark with 198461 gates is evaluated in 3 minutes with the proposed approach compared to 23 hours for random sampling with comparable accuracy. Compared to a conventional analytical approach using Wilkinson's approximation, the proposed technique offers superior accuracy while maintaining efficiency. State dependence and multiple sources of variation are considered and the approach is scalable with number of process parameter variables for standard cell characterization cost. We also show reduction in sample size to meet target accuracy for computing leakage distribution due to the inter-die component only when compared to random selection of samples.
Starting Page	154
Ending Page	159
File Size	1617492
Page Count	6
File Format	PDF
ISBN	9781605584973
ISSN	0738100X
DOI	10.1145/1629911.1629956
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Association for Computing Machinery, Inc. (ACM)
Subject Keyword	Sampling methods Monte Carlo methods Costs Distributed computing Leakage current Space technology Design for manufacture Probability distribution Linear approximation Chip scale packaging Statistical leakage Monte Carlo Variance reduction
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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