Optimizing automatic abstraction refinement for generalized symbolic trajectory evaluation

  • Authors:
  • Yan Chen;Fei Xie;Jin Yang

  • Affiliations:
  • Portland State University, Portland, OR;Portland State University, Portland, OR;Intel Corporation, Hillsboro, OR

  • Venue:
  • Proceedings of the 45th annual Design Automation Conference
  • Year:
  • 2008

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Abstract

In this paper, we present a suite of optimizations targeting automatic abstraction refinement for Generalized Symbolic Trajectory Evaluation (GSTE). We optimize both model refinement and spec refinement supported by AutoGSTE: a counterexample-guided refinement loop for GSTE. Experiments on a family of benchmark circuits have shown that our optimizations lead to major efficiency improvements in verification involving abstraction refinement.