Article

Mixture importance sampling and its application to the analysis of SRAM designs in the presence of rare failure events

Authors:

Rouwaida Kanj,

Rajiv Joshi,

Sani NassifAuthors Info & Claims

DAC '06: Proceedings of the 43rd annual Design Automation Conference

Pages 69 - 72

https://doi.org/10.1145/1146909.1146930

Published: 24 July 2006 Publication History

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Abstract

In this paper, we propose a novel methodology for statistical SRAM design and analysis. It relies on an efficient form of importance sampling, mixture importance sampling. The method is comprehensive, computationally efficient and the results are in excellent agreement with those obtained via standard Monte Carlo techniques. All this comes at significant gains in speed and accuracy, with speedup of more than 100X compared to regular Monte Carlo. To the best of our knowledge, this is the first time such a methodology is applied to the analysis of SRAM designs.

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Cited By

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Gao ZZhang DDaniel LBoning DDe V(2024)NOFIS: Normalizing Flow for Rare Circuit Failure AnalysisProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3658459(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3658459
Guo ZSun WWang ZCai YShi L(2024)An Efficient SRAM Yield Analysis Method using Multi -Fidelity Neural Network2024 2nd International Symposium of Electronics Design Automation (ISEDA)10.1109/ISEDA62518.2024.10617638(547-551)Online publication date: 10-May-2024
https://doi.org/10.1109/ISEDA62518.2024.10617638
Bouhlila JLast FBuchty RBerekovic MMulhem S(2024)Machine Learning for SRAM Stability Analysis2024 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS58744.2024.10558564(1-5)Online publication date: 19-May-2024
https://doi.org/10.1109/ISCAS58744.2024.10558564
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Index Terms

Mixture importance sampling and its application to the analysis of SRAM designs in the presence of rare failure events
1. Hardware
  1. Electronic design automation
    1. Physical design (EDA)

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Published In

DAC '06: Proceedings of the 43rd annual Design Automation Conference

July 2006

1166 pages

ISBN:1595933816

DOI:10.1145/1146909

General Chair:
Ellen M. Sentovich
Cadence Berkeley Labs, Berkeley, CA

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: 24 July 2006

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July 24 - 28, 2006

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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Gao ZZhang DDaniel LBoning DDe V(2024)NOFIS: Normalizing Flow for Rare Circuit Failure AnalysisProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3658459(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3658459
Guo ZSun WWang ZCai YShi L(2024)An Efficient SRAM Yield Analysis Method using Multi -Fidelity Neural Network2024 2nd International Symposium of Electronics Design Automation (ISEDA)10.1109/ISEDA62518.2024.10617638(547-551)Online publication date: 10-May-2024
https://doi.org/10.1109/ISEDA62518.2024.10617638
Bouhlila JLast FBuchty RBerekovic MMulhem S(2024)Machine Learning for SRAM Stability Analysis2024 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS58744.2024.10558564(1-5)Online publication date: 19-May-2024
https://doi.org/10.1109/ISCAS58744.2024.10558564
Shukla AShrivastava SSaran SSingh GDhiman G(2024)Dynamic Characterization of Proposed SRAM Cell at 16nm Technology2024 First International Conference on Electronics, Communication and Signal Processing (ICECSP)10.1109/ICECSP61809.2024.10698273(1-6)Online publication date: 8-Aug-2024
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A fast estimation of SRAM failure rate using probability collectives

Adaptive Clustering and Sampling for High-Dimensional and Multi-Failure-Region SRAM Yield Analysis

Importance sampling: a review

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A fast estimation of SRAM failure rate using probability collectives

Adaptive Clustering and Sampling for High-Dimensional and Multi-Failure-Region SRAM Yield Analysis

Importance sampling: a review

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