No abstract

Abstract. We propose algorithms significantly extending the limits for maintaining exact representations in the verification of linear hybrid systems with large discrete state spaces. We use AND-Inverter Graphs (AIGs) extended with linear constraints (LinAIGs) as symbolic representation of the hybrid state space, and show how methods for maintaining compactness of AIGs can be lifted to support model-checking of linear hybrid systems with large discrete state spaces. This builds on a novel approach for eliminating sets of redundant constraints in such rich hybrid state representations by a suitable exploitation of the capabilities of SMT solvers, which is of independent value beyond the application context studied in this paper. We used a benchmark derived from an Airbus flap control system (containing 2 20 discrete states) to demonstrate the relevance of the approach.

No abstract

In this paper we present a complete method for verifying properties expressed in the temporal logic CTL. In contrast to the majority of verification methods presented in recent years, we support unbounded model checking based on symbolic representations of characteristic functions. Among others, our method is based on an advanced And-Inverter Graph (AIG) implementation, quantifier scheduling, and BDD sweeping. For several examples, our method outperforms BDD based symbolic model checking by orders of magnitude. However, our approach is also able to produce competitive results for cases where BDD are known to perform well.

No abstract

Pass Transistor Logic has attracted more and more interest during last years, since it has proved to be an attractive alternative to static CMOS designs with respect to area, performance and power consumption. Existing automatic PTL synthesis tools use a direct mapping of (decomposed) BDDs to pass transistors. Thereby, structural properties of BDDs like the ordering restriction and the fact that the select signals of the multiplexers (corresponding to BDD nodes) directly depend on input variables, are imposed on PTL circuits although they are not necessary for PTL synthesis.General Multiplexer Circuits can be used instead and should provide a much higher potential for optimization compared to a pure BDD approach. Nevertheless -to the best of our knowledge -an optimization of general Multiplexer Circuits (MCs) for PTL synthesis was not tried so far due to a lack of suitable optimization approaches. In this paper we present such an algorithm which is based on efficient BDD optimization techniques. Our experiments prove that there is indeed a high optimization potential by the use of general MCs -both concerning area and depth of the resulting PTL networks.

Abstract.We address the problem of model checking hybrid systems which exhibit nontrivial discrete behavior and thus cannot be treated by considering the discrete states one by one, as most currently available verification tools do. Our procedure relies on a deep integration of several techniques and tools. An extension of AND-Inverter-Graphs (AIGs) with first-order constraints serves as a compact representation format for sets of configurations which are composed of continuous regions and discrete states. Boolean reasoning on the AIGs is complemented by firstorder reasoning in various forms and on various levels. These include implication checks for simple constraints, test vector generation for fast inequality checks of boolean combinations of constraints, and an exact subsumption check for representations of two configurations.These techniques are integrated within a model checker for universal CTL. Technically, it deals with discrete-time hybrid systems with linear differentials. The paper presents the approach, its prototype implementation, and first experimental data.

In this paper we present a solver for Quantified Boolean Formulas (QBFs) which is based on And-Inverter Graphs (AIGs). We use a new quantifier elimination method for AIGs, which heuristically combines cofactor-based quantifier elimination with quantification using BDDs and thus benefits from the strengths of both data structures. Moreover, we present a novel SAT-based method for preprocessing QBFs that is able to efficiently detect variables with forced truth assignments, allowing for an elimination of these variables from the input formula. We describe the used algorithm which heavily relies on the incremental features of modern SATsolvers. Experimental results demonstrate that our preprocessing method can significantly improve the performance of QBF preprocessing and thus is able to accelerate the overall solving process when used in combination with state-of-the-art QBF-solvers. In particular, we integrated the preprocessing technique as well as the quantifier elimination method into the QBF-solver AIGSolve, allowing it to outperform state-of-the-art solvers.