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Optimizing automatic abstraction refinement for generalized symbolic trajectory evaluation
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Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 45th annual Design Automation Conference table of contents
Anaheim, California
SESSION: Formal verification technology table of contents
Pages 143-148  
Year of Publication: 2008
ISBN ~ ISSN:0738-100X , 978-1-60558-115-6
Authors
Yan Chen  Portland State University, Portland, OR
Fei Xie  Portland State University, Portland, OR
Jin Yang  Intel Corporation, Hillsboro, OR
Sponsors
SIGDA: ACM Special Interest Group on Design Automation
: IEEE/CASS/CANDE/CEDA
: The EDA Consortium
Publisher
ACM  New York, NY, USA
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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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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