Refinement strategies for verification methods based on datapath abstraction

Authors:
Zaher S. Andraus;Mark H. Liffiton;Karem A. Sakallah
Affiliations:
University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI
Venue:
ASP-DAC '06 Proceedings of the 2006 Asia and South Pacific Design Automation Conference
Year:
2006

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Abstract

In this paper we explore the application of Counter example-Guided Abstraction Refinement (CEGAR) in the context of microprocessor correspondence checking. The approach utilizes automatic datapath abstraction, augmented with automatic refinement based on 1) localization, 2) generalization, and 3) minimal unsatisfiable subset (MUS) extraction. We introduce several refinement strategies and empirically evaluate their effectiveness on a set of microprocessor benchmarks. The data suggest that localization, generalization, and MUS extraction from both the abstract and concrete models are essential for effective verification. Additionally, refinement tends to converge faster when multiple MUses are extracted in each iteration.