research-article

Classifying circuit performance using active-learning guided support vector machines

Authors:

Peng LiAuthors Info & Claims

ICCAD '12: Proceedings of the International Conference on Computer-Aided Design

Pages 187 - 194

https://doi.org/10.1145/2429384.2429420

Published: 05 November 2012 Publication History

Abstract

Leveraging machine learning has been proven as a promising avenue for addressing many practical circuit design and verification challenges. We demonstrate a novel active learning guided machine learning approach for characterizing circuit performance. When employed under the context of support vector machines, the proposed probabilistically weighted active learning approach is able to dramatically reduce the size of the training data, leading to significant reduction of the overall training cost. The proposed active learning approach is extended to the training of asymmetric support vector machine classifiers, which is further sped up by a global acceleration scheme. We demonstrate the excellent performance of the proposed techniques using three case studies: PLL lock-time verification, SRAM yield analysis and prediction of chip peak temperature using a limited number of on-chip temperature sensors.

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Cited By

Lin YLi MWatanabe YKimura TMatsunawa TNojima SPan D(2019)Data Efficient Lithography Modeling With Transfer Learning and Active Data SelectionIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2018.286425138:10(1900-1913)Online publication date: Oct-2019
https://doi.org/10.1109/TCAD.2018.2864251
Wang MYan CLi XZhou DZeng X(2017)High-Dimensional and Multiple-Failure-Region Importance Sampling for SRAM Yield AnalysisIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2016.260160625:3(806-819)Online publication date: Mar-2017
https://doi.org/10.1109/TVLSI.2016.2601606
Mukherjee PLi P(2014)Leveraging pre-silicon data to diagnose out-of-specification failures in mixed-signal circuitsProceedings of the 51st Annual Design Automation Conference10.1145/2593069.2593154(1-6)Online publication date: 1-Jun-2014
https://dl.acm.org/doi/10.1145/2593069.2593154
Show More Cited By

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Classifying circuit performance using active-learning guided support vector machines

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cover image ACM Conferences

ICCAD '12: Proceedings of the International Conference on Computer-Aided Design

November 2012

781 pages

ISBN:9781450315739

DOI:10.1145/2429384

General Chair:
Alan J. Hu
Univ. of British Columbia, Vancouver, BC, Canada

Copyright © 2012 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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SIGDA: ACM Special Interest Group on Design Automation
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Association for Computing Machinery

New York, NY, United States

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Published: 05 November 2012

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ICCAD '12

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ICCAD '12: The International Conference on Computer-Aided Design

November 5 - 8, 2012

California, San Jose

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Overall Acceptance Rate 457 of 1,762 submissions, 26%

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October 27 - 31, 2024

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Cited By

Lin YLi MWatanabe YKimura TMatsunawa TNojima SPan D(2019)Data Efficient Lithography Modeling With Transfer Learning and Active Data SelectionIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2018.286425138:10(1900-1913)Online publication date: Oct-2019
https://doi.org/10.1109/TCAD.2018.2864251
Wang MYan CLi XZhou DZeng X(2017)High-Dimensional and Multiple-Failure-Region Importance Sampling for SRAM Yield AnalysisIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2016.260160625:3(806-819)Online publication date: Mar-2017
https://doi.org/10.1109/TVLSI.2016.2601606
Mukherjee PLi P(2014)Leveraging pre-silicon data to diagnose out-of-specification failures in mixed-signal circuitsProceedings of the 51st Annual Design Automation Conference10.1145/2593069.2593154(1-6)Online publication date: 1-Jun-2014
https://dl.acm.org/doi/10.1145/2593069.2593154
Mukherjee PAmin CLi P(2014)Approximate property checking of mixed-signal circuitsProceedings of the 51st Annual Design Automation Conference10.1145/2593069.2593091(1-6)Online publication date: 1-Jun-2014
https://dl.acm.org/doi/10.1145/2593069.2593091
Yao JYe ZWang Y(2014)Importance Boundary Sampling for SRAM Yield Analysis With Multiple Failure RegionsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2013.229250433:3(384-396)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1109/TCAD.2013.2292504
Lin HLi PMyers C(2013)Verification of digitally-intensive analog circuits via kernel ridge regression and hybrid reachability analysisProceedings of the 50th Annual Design Automation Conference10.1145/2463209.2488814(1-6)Online publication date: 29-May-2013
https://dl.acm.org/doi/10.1145/2463209.2488814
Goudjil MKoudil MHammami NBedda MAlruily M(2013)Arabic text categorization using SVM active learning technique: An overview2013 World Congress on Computer and Information Technology (WCCIT)10.1109/WCCIT.2013.6618666(1-2)Online publication date: Jun-2013
https://doi.org/10.1109/WCCIT.2013.6618666

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