Checking Safety Properties Using Induction and a SAT-Solver
FMCAD '00 Proceedings of the Third International Conference on Formal Methods in Computer-Aided Design
Modeling and Verification of Out-of-Order Microprocessors in UCLID
FMCAD '02 Proceedings of the 4th International Conference on Formal Methods in Computer-Aided Design
Iterative Abstraction using SAT-based BMC with Proof Analysis
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Word level predicate abstraction and refinement for verifying RTL verilog
Proceedings of the 42nd annual Design Automation Conference
Hybrid CEGAR: combining variable hiding and predicate abstraction
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Automated abstraction by incremental refinement in interpolant-based model checking
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
Strengthening model checking techniques with inductive invariants
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Automatic abstraction without counterexamples
TACAS'03 Proceedings of the 9th international conference on Tools and algorithms for the construction and analysis of systems
Analyzing k-step induction to compute invariants for SAT-based property checking
Proceedings of the 47th Design Automation Conference
Unbounded Protocol Compliance Verification Using Interval Property Checking With Invariants
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
QuteRTL: towards an open source framework for RTL design synthesis and verification
TACAS'12 Proceedings of the 18th international conference on Tools and Algorithms for the Construction and Analysis of Systems
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In this paper, we propose a property-specific sequential invariant extraction algorithm to improve the performance of the SAT-based Unbounded Modeling Checkers (UMCs). By analyzing the property-related predicates and their corresponding high-level design constructs such as FSMs and counters, we can quickly identify the sequential invariants that are useful in improving the property proving capabilities. We utilize these sequential invariants to refine the inductive hypothesis in induction-based UMCs, and to improve the accuracy of reachable state approximation in interpolation-based UMCs. The experimental results show that our tool can outperform a state-of-the-art UMC in most cases, especially for the difficult true properties.