A performance-driven QBF-based iterative logic array representation with applications to verification, debug and test

Mangassarian,; Veneris,; Safarpour,; Benedetti,; Smith, Stephen L.

doi:10.1109/iccad.2007.4397272

Cited by 27 publications

(35 citation statements)

References 17 publications

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“…The first aim of this paper is the development of a new, performance-driven mathematical model that encodes the sequential behavior of a design using QBF [28]. The encoding uses a single copy of the design and circumvents the memory-intensive circuit replication inherent in SAT-based representations using an ILA.…”

Section: • H Mangassarian Is With the Department Of Electrical And Cmentioning

confidence: 99%

Robust QBF Encodings for Sequential Circuits with Applications to Verification, Debug, and Test

Mangassarian

Veneris

Benedetti

2010

IEEE Trans. Comput.

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Abstract-Formal CAD tools operate on mathematical models describing the sequential behavior of a VLSI design. With the growing size and state-space of modern digital hardware designs, the conciseness of this mathematical model is of paramount importance in extending the scalability of those tools, provided that the compression does not come at the cost of reduced performance. Quantified Boolean Formula satisfiability (QBF) is a powerful generalization of Boolean satisfiability (SAT). It also belongs to the same complexity class as many CAD problems dealing with sequential circuits, which makes it a natural candidate for encoding such problems. This work proposes a succinct QBF encoding for modeling sequential circuit behavior. The encoding is parametrized and further compression is achieved using time-frame windowing. Comprehensive hardware constructions are used to illustrate the proposed encodings. Three notable CAD problems, namely bounded model checking, design debugging and sequential test pattern generation, are encoded as QBF instances to demonstrate the robustness and practicality of the proposed approach. Extensive experiments on OpenCore circuits show memory reductions in the order of 90% and demonstrate competitive run-times compared to state-of-the-art SAT techniques. Furthermore, the number of solved instances is increased by 16%. Admittedly, this work encourages further research in the use of QBF in CAD for VLSI.

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Section: • H Mangassarian Is With the Department Of Electrical And Cmentioning

confidence: 99%

Robust QBF Encodings for Sequential Circuits with Applications to Verification, Debug, and Test

Mangassarian

Veneris

Benedetti

2010

IEEE Trans. Comput.

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“…QBF provides a powerful framework for encoding many important verification and reasoning problems like model checking or scheduling [2]. QBF extends propositional logic with existential and universal quantifiers, which allow a more compact representation for problems with adversarial knowledge, incomplete information, or nondeterministic behavior, especially when the propositional form becomes too large to be handled efficiently [11].…”

Section: Introductionmentioning

confidence: 99%

Bridging the Gap between Dual Propagation and CNF-based QBF Solving

Goultiaeva¹,

Seidl²,

Biere³

2013

Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 2013

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Abstract-Conjunctive Normal Form (CNF) representation as used by most modern Quantified Boolean Formula (QBF) solvers is simple and powerful when reasoning about conflicts, but is not efficient at dealing with solutions. To overcome this inefficiency a number of specialized non-CNF solvers were created. These solvers were shown to have great advantages. Unfortunately, non-CNF solvers cannot benefit from sophisticated CNF-based techniques developed over the years. This paper demonstrates how the power of non-CNF structure can be harvested without the need for specialized solvers; in fact, it is easily incorporated into most existing CNF-based QBF solvers using a pre-existing mechanism of cube learning. We demonstrate this using a state-of-the-art QBF solver DepQBF, and experimentally show the effectiveness of our approach.

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“…The addition of quantifiers, and the arbitrary nesting of quantifiers, provides considerable additional representational power: QBFs can compactly represent a much wider range of problems than SAT. This can make QBF more effective than SAT for representing and solving some problems [1].…”

Section: Introductionmentioning

confidence: 99%

“…1 The truth value of a QBF is defined recursively: ∃xQ.φ is true iff there is at least one value v of x for which Q.φ| x=v is true, and ∀xQ.φ is true iff Q.φ| x=v is true for both values v of x.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Circuit Representations in QBF Solving

Goultiaeva¹,

Bacchus²

2010

Theory and Applications of Satisfiability Testing – SAT 2010

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Abstract.Previous work has shown that circuit representations can be exploited in QBF solvers to obtain useful performance improvements. In this paper we examine some additional techniques for exploiting a circuit representations. We discuss the techniques of propagating a dual set of values through the circuit, conversion from simple negation normal form to a more optimized circuit representation, and adding phase memorization during search. We have implemented these techniques in a new QBF solver called CirQit2 and evaluated their impact experimentally. The solver has also displayed superior performance in the nonprenex non-CNF track of the QBFEval'10 competition.

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A performance-driven QBF-based iterative logic array representation with applications to verification, debug and test

Cited by 27 publications

References 17 publications

Robust QBF Encodings for Sequential Circuits with Applications to Verification, Debug, and Test

Robust QBF Encodings for Sequential Circuits with Applications to Verification, Debug, and Test

Bridging the Gap between Dual Propagation and CNF-based QBF Solving

Exploiting Circuit Representations in QBF Solving

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