Harnessing machine learning to improve the success rate of stimuli generation

Shai Fine, Ari Freund, Itai Jaeger, Yishay Mansour, Yehuda Naveh, Avi Ziv

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)1344-1355
Number of pages12
JournalIEEE Transactions on Computers
Volume55
Issue number11
DOIs
StatePublished - Nov 2006

Keywords

  • Bayesian networks
  • Coverage analysis
  • Coverage directed generation
  • Fourier transforms
  • Functional verification
  • Machine learning

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