Accelerated verification of RTL assertions based on satisfiability solvers

Fraer, Ranan; Ikram, S.; Kamhi, Gila; Leonard, Tim; Mokkedem, Abdel

doi:10.1109/hldvt.2002.1224437

Cited by 9 publications

(9 citation statements)

References 7 publications

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“…Fraer et al [FIK+02] proposed a method for simultaneously checking a number of safety properties P 1 ,…,P n on the same model. Their idea is to form a conjunction P from the properties P i .…”

Section: The Previous Work On Simultaneous Inductionmentioning

confidence: 99%

“…Similarly, the STEP_CONJUNCTION algorithm below corresponds to the way simultaneous_step procedure is performed in [FIK+02]: Here M is the model returned by the SAT solver, and step_model_callback checks which of the variables representing properties in V at depth k + 1 are false in M; such properties are moved from V to U, as we know they cannot be proven at depth k; valid_callback will report all properties in V valid to the user (the list V may be empty after STEP_CONJUNCTION terminates).…”

Section: Simultaneous-induction(umentioning

confidence: 99%

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Simultaneous SAT-Based Model Checking of Safety Properties

Khasidashvili

Nadel

Palti

et al. 2006

Lecture Notes in Computer Science

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Abstract. We present several algorithms for simultaneous SAT (propositional satisfiability) based model checking of safety properties. More precisely, we focus on Bounded Model Checking and Temporal Induction methods for simultaneously verifying multiple safety properties on the same model. The most efficient among our proposed algorithms for model checking are based on a simultaneous propositional satisfiability procedure (SSAT for short), which we design for solving related propositional objectives simultaneously, by sharing the learned clauses and the search. The SSAT algorithm is fully incremental in the sense that all clauses learned while solving one objective can be reused for the remaining objectives. Furthermore, our SSAT algorithm ensures that the SSAT solver will never re-visit the same sub-space during the search, even if there are several satisfiability objectives, hence one traversal of the search space is enough. Finally, in SSAT all SAT objectives are watched simultaneously, thus we can solve several other SAT objectives when the search is oriented to solve a particular SAT objective first. Experimental results on Intel designs demonstrate that our new algorithms can be orders of magnitude faster than the previously known techniques in this domain.

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Section: The Previous Work On Simultaneous Inductionmentioning

confidence: 99%

Section: Simultaneous-induction(umentioning

confidence: 99%

Simultaneous SAT-Based Model Checking of Safety Properties

Khasidashvili

Nadel

Palti

et al. 2006

Lecture Notes in Computer Science

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“…By invCON J, we refer to the invariant strengthening scheme where saturation is replaced by SAT. That is, in invCON J, the candidate invariants are proven using the conjunction approach to verifying multiple properties presented in [11]. Table 3.…”

Section: Ssat → and Strengthening Inductive Invariantsmentioning

confidence: 99%

“…One is in-depth BMC, which uses simultaneous solving in a BMC [2] scheme where unrolling happens with intervals [11,28] (the SAT solver is not called after each unrolling step). The other application is an invariant strengthening algorithm for equivalence checking known as van Eijk's method [10].…”

Section: Introductionmentioning

confidence: 99%

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Implicative Simultaneous Satisfiability and Applications

Khasidashvili

Nadel

2012

Hardware and Software: Verification and Testing

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Abstract. This paper proposes an efficient algorithm for the systematic learning of implications. This is done as part of a new search and restart strategy in the SAT solver. We evaluate the new algorithm within a number of applications, including BMC and induction with invariant strengthening for equivalence checking. We provide extensive experimental evidence attesting to a speedup of one and often two orders of magnitude with our algorithm, on a representative set of industrial and publicly available test suites, as compared to a basic version of invariant strengthening. Moreover, we show that the new invariant strengthening algorithm alone performs better than induction and interpolation, and that the absolutely best result is achieved when it is combined with interpolation. In addition, we experimentally demonstrate the superiority of an application of our new algorithm to BMC.

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