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Published Articles >> Table of Contents >> Abstract
November 2006 (Vol. 55, No. 11)
pp. 1344-1355
Harnessing Machine Learning to Improve the Success Rate of Stimuli Generation
Shai Fine
Ari Freund
Itai Jaeger
Yishay Mansour
Yehuda Naveh
Avi Ziv, IEEE
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TC.2006.183
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| Abstract |
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The initial state of a design under verification has a major impact on the ability of stimuli generators to successfully generate the requested stimuli. For complexity reasons, most stimuli generators use sequential solutions without planning ahead. Therefore, in many cases, they fail to produce a consistent stimuli due to an inadequate selection of the initial state. We propose a new method, based on machine learning techniques, to improve generation success by learning the relationship between the initial state vector and generation success. We applied the proposed method in two different settings, with the objective of improving generation success and coverage in processor and system level generation. In both settings, the proposed method significantly reduced generation failures and enabled faster coverage.
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 Additional Information
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Index Terms- Functional verification, coverage analysis, coverage directed generation, machine learning, Bayesian networks, Fourier transforms.
Citation:
Shai Fine, Ari Freund, Itai Jaeger, Yishay Mansour, Yehuda Naveh, Avi Ziv,
"Harnessing Machine Learning to Improve the Success Rate of Stimuli Generation,"
IEEE Transactions on Computers,
vol. 55,
no. 11,
pp. 1344-1355,
Nov.,
2006
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