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Full-chip leakage current estimation based on statistical sampling techniques

Authors:

Qing WuAuthors Info & Claims

GLSVLSI '08: Proceedings of the 18th ACM Great Lakes symposium on VLSI

Pages 391 - 394

https://doi.org/10.1145/1366110.1366203

Published: 04 May 2008 Publication History

Abstract

In this paper, we propose statistical sampling techniques in estimating the mean and distribution of full-chip leakage current under process variations. The stratified random sampling procedures are used to estimate the mean and variance of the full-chip leakage, under intra-die and inter-die process variations. Statistical quantile estimation method is then applied to estimate the cumulative distribution function. Experimental results show that, comparing to simple random sampling, the proposed approaches improve the estimation speed by 2.7X, on average.

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Cited By

(2018)Variability aware transistor stack based regression surrogate models for accurate and efficient statistical leakage estimationMicroelectronics Journal10.1016/j.mejo.2017.05.01569:C(1-19)Online publication date: 26-Dec-2018
https://dl.acm.org/doi/10.1016/j.mejo.2017.05.015
Liu SJingyi Zhang Qing Wu Qinru Qiu (2010)Thermal-aware job allocation and scheduling for three dimensional chip multiprocessor2010 11th International Symposium on Quality Electronic Design (ISQED)10.1109/ISQED.2010.5450547(390-398)Online publication date: Mar-2010
https://doi.org/10.1109/ISQED.2010.5450547
Liu SQiu MGao WTang XGuo B(2010)Hybrid of Job Sequencing and DVFS for Peak Temperature Reduction with Nondeterministic ApplicationsProceedings of the 2010 10th IEEE International Conference on Computer and Information Technology10.1109/CIT.2010.309(1780-1787)Online publication date: 29-Jun-2010
https://dl.acm.org/doi/10.1109/CIT.2010.309

Index Terms

Full-chip leakage current estimation based on statistical sampling techniques
1. Hardware
  1. Hardware validation

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cover image ACM Conferences

GLSVLSI '08: Proceedings of the 18th ACM Great Lakes symposium on VLSI

May 2008

480 pages

ISBN:9781595939999

DOI:10.1145/1366110

General Chairs:
Vijay Narayanan
Penn State University
,
Zhiyuan Yan
Lehigh University
,
Program Chairs:
Enrico Macii
Politecnico di Torino
,
Sanjukta Bhanja
University of South Florida

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 04 May 2008

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May 4 - 6, 2008

Florida, Orlando, USA

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Overall Acceptance Rate 312 of 1,156 submissions, 27%

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Cited By

(2018)Variability aware transistor stack based regression surrogate models for accurate and efficient statistical leakage estimationMicroelectronics Journal10.1016/j.mejo.2017.05.01569:C(1-19)Online publication date: 26-Dec-2018
https://dl.acm.org/doi/10.1016/j.mejo.2017.05.015
Liu SJingyi Zhang Qing Wu Qinru Qiu (2010)Thermal-aware job allocation and scheduling for three dimensional chip multiprocessor2010 11th International Symposium on Quality Electronic Design (ISQED)10.1109/ISQED.2010.5450547(390-398)Online publication date: Mar-2010
https://doi.org/10.1109/ISQED.2010.5450547
Liu SQiu MGao WTang XGuo B(2010)Hybrid of Job Sequencing and DVFS for Peak Temperature Reduction with Nondeterministic ApplicationsProceedings of the 2010 10th IEEE International Conference on Computer and Information Technology10.1109/CIT.2010.309(1780-1787)Online publication date: 29-Jun-2010
https://dl.acm.org/doi/10.1109/CIT.2010.309

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