Abstraction Refinement for Large Scale Model Checking [electronic resource] / by Chao Wang, Gary D. Hachtel, Fabio Somenzi.

9780387346007

Boston, MA : Springer US, 2006.

1 online resource (XIV, 179 p.)

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Abstraction Refinement for Large Scale Model Checking summarizes recent research on abstraction techniques for model checking large digital systems. Considering both the size of today's digital systems and the capacity of state-of-the-art verification algorithms, abstraction is the only viable solution for the successful application of model checking techniques to industrial-scale designs. This book describes recent research developments in automatic abstraction refinement techniques. The authors address the main challenge in abstraction refinement, i.e., the ability to efficiently reach or come close to the optimum abstraction (the smallest abstract model that proves or refutes the given property). A suite of fully automatic abstraction techniques are proposed to improve the overall computation efficiency. The suite of algorithms presented in this book has demonstrated significant improvement over the prior art; some of them have already been adopted by the EDA companies in their commercial/in-house verification tools. Abstraction Refinement for Large Scale Model Checking will be of interest to EDA researchers and tool developers, verification engineers, as well as people who are in the general areas of computer science and want to know the state-of-the-art of formal verification.

Springer ENIN.

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English

June 26, 2019