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Kwangting Cheng - One of the best experts on this subject based on the ideXlab platform.

  • using word level atpg and modular Arithmetic Constraint solving techniques for assertion property checking
    IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2001
    Co-Authors: Chungyang Huan, Kwangting Cheng
    Abstract:

    We present a new approach to checking assertion properties for register-transfer level (RTL) design verification. Our approach combines structural word-level automatic test pattern generation (ATPG) and modular Arithmetic Constraint-solving techniques to solve the Constraints imposed by the target assertion property. Our word-level ATPG and implication technique not only solves the Constraints on the control logic, but also propagates the logic implications to the datapath. A novel Arithmetic Constraint solver based on modular number system is then employed to solve the remaining Constraints in datapath. The advantages of the new method are threefold. First, the decision-making process of the word-lever ATPG is confined to the selected control signals only. Therefore, the enumeration of enormous number of choices at the datapath signals is completely avoided. Second, our new implication translation techniques allow word-level logic implication being performed across the boundary of datapath and control logic and, therefore, efficiently cut down the ATPG search space. Third, our Arithmetic Constraint solver is based on modular instead of integral number systems. It can thus avoid the false-negative effect resulting from the bit-vector value modulation. A prototype system has been built that consists of an industrial front-end hardware description language (HDL) parser, a property-to-Constraint converter, and the ATPG/Arithmetic Constraint-solving engine. The experimental results on some public benchmark and industrial circuits demonstrate the efficiency of our approach and its applicability to large industrial designs.

  • assertion checking by combined word level atpg and modular Arithmetic Constraint solving techniques
    Design Automation Conference, 2000
    Co-Authors: Chungyang Huang, Kwangting Cheng
    Abstract:

    We present a new approach to checking assertion properties for RTI, design verification. Our approach combines structural, word-level automatic test pattern generation (ATPG) and modular Arithmetic Constraint-solving techniques to solve the Constraints imposed by the target assertion property. Our word-level ATPG and implication technique not only solves the Constraints on the control logic, but also propagates the logic implications to the datapath. A novel Arithmetic Constraint solver based on modular number system is then employed to solve the remaining Constraints in datapath. The advantages of the new method are threefold. First, the decision-making process of the word-level ATPG is confined to the selected control signals only. Therefore, the enumeration of enormous number of choices at the datapath signals is completely avoided. Second, our new implication translation techniques allow word-level logic implication being performed across the boundary of datapath and control logic, and therefore, efficiently cut down the ATPG search space. Third, our Arithmetic Constraint solver is based on modular instead of integral number system. It can thus avoid the false negative effect resulting from the bit-vector value modulation. A prototype system has been built which consists of an industrial front-end HDL parser, a property-to-Constraint converter and the ATPG/Arithmetic Constraint-solving engine. The experimental results on some public benchmark and industrial circuits demonstrate the efficiency of our approach and its applicability to large industrial designs.

Chungyang Huang - One of the best experts on this subject based on the ideXlab platform.

  • assertion checking by combined word level atpg and modular Arithmetic Constraint solving techniques
    Design Automation Conference, 2000
    Co-Authors: Chungyang Huang, Kwangting Cheng
    Abstract:

    We present a new approach to checking assertion properties for RTI, design verification. Our approach combines structural, word-level automatic test pattern generation (ATPG) and modular Arithmetic Constraint-solving techniques to solve the Constraints imposed by the target assertion property. Our word-level ATPG and implication technique not only solves the Constraints on the control logic, but also propagates the logic implications to the datapath. A novel Arithmetic Constraint solver based on modular number system is then employed to solve the remaining Constraints in datapath. The advantages of the new method are threefold. First, the decision-making process of the word-level ATPG is confined to the selected control signals only. Therefore, the enumeration of enormous number of choices at the datapath signals is completely avoided. Second, our new implication translation techniques allow word-level logic implication being performed across the boundary of datapath and control logic, and therefore, efficiently cut down the ATPG search space. Third, our Arithmetic Constraint solver is based on modular instead of integral number system. It can thus avoid the false negative effect resulting from the bit-vector value modulation. A prototype system has been built which consists of an industrial front-end HDL parser, a property-to-Constraint converter and the ATPG/Arithmetic Constraint-solving engine. The experimental results on some public benchmark and industrial circuits demonstrate the efficiency of our approach and its applicability to large industrial designs.

Martin Franzle - One of the best experts on this subject based on the ideXlab platform.

  • proofs of unsatisfiability for mixed boolean and non linear Arithmetic Constraint formulae
    MBMV, 2009
    Co-Authors: Stefan Kupferschmid, Tino Teige, Bernd Becker, Martin Franzle
    Abstract:

    Symbolic methods in computer-aided verification rely on appropriate Constraint solvers. Correctness and reliability of solvers are a vital requirement in the analysis of safety-critical systems, e.g., in the automotive context. Satisfiability results of a solver can usually be checked by probing the computed solution. However, efficient validation of an uncertified unsatisfiability result for some Constraint formula is nearly impossible. In this paper, we propose a certification method for unsatisfiability results for mixed Boolean and non-linear Arithmetic Constraint formulae. Such formulae arise in the analysis of hybrid discrete-continuous systems.

  • sat modulo ode a direct sat approach to hybrid systems
    Automated Technology for Verification and Analysis, 2008
    Co-Authors: Andreas Eggers, Martin Franzle, Christian Herde
    Abstract:

    In order to facilitate automated reasoning about large Boolean combinations of non-linear Arithmetic Constraints involving ordinary differential equations (ODEs), we provide a seamless integration of safe numeric overapproximation of initial-value problems into a SAT-modulo-theory (SMT) approach to interval-based Arithmetic Constraint solving. Interval-based safe numeric approximation of ODEs is used as an interval contractor being able to narrow candidate sets in phase space in both temporal directions: post-images of ODEs (i.e., sets of states reachable from a set of initial values) are narrowed based on partial information about the initial values and, vice versa, pre-images are narrowed based on partial knowledge about post-sets. In contrast to the related CLP(F) approach of Hickey and Wittenberg [12], we do (a) support coordinate transformations mitigating the wrapping effect encountered upon iterating interval-based overapproximations of reachable state sets and (b) embed the approach into an SMT framework, thus accelerating the solving process through the algorithmic enhancements of recent SAT solving technology.

  • stochastic satisfiability modulo theory a novel technique for the analysis of probabilistic hybrid systems
    ACM International Conference Hybrid Systems: Computation and Control, 2008
    Co-Authors: Martin Franzle, Holger Hermanns, Tino Teige
    Abstract:

    The analysis of hybrid systems exhibiting probabilistic behaviour is notoriously difficult. To enable mechanised analysis of such systems, we extend the reasoning power of Arithmetic satisfiability-modulo-theory solving (SMT) by a comprehensive treatment of randomized (a.k.a. stochastic) quantification over discrete variables within the mixed Boolean-Arithmetic Constraint system. This provides the technological basis for a fully symbolic analysis of probabilistic hybrid automata. Generalizing SMT-based bounded model-checking of hybrid automata [2,11], stochastic SMT permits the direct and fully symbolic analysis of probabilistic bounded reachability problems of probabilistic hybrid automata without resorting to approximation by intermediate finite-state abstractions.

  • efficient solving of large non linear Arithmetic Constraint systems with complex boolean structure
    Journal on Satisfiability Boolean Modeling and Computation, 2007
    Co-Authors: Martin Franzle, Christian Herde, Tino Teige, Stefan Ratschan, Tobias Schubert
    Abstract:

    In order to facilitate automated reasoning about large Boolean combinations of nonlinear Arithmetic Constraints involving transcendental functions, we provide a tight integration of recent SAT solving techniques with interval-based Arithmetic Constraint solving. Our approach deviates substantially from lazy theorem proving approaches in that it directly controls Arithmetic Constraint propagation from the SAT solver rather than delegating Arithmetic decisions to a subordinate solver. Through this tight integration, all the algorithmic enhancements that were instrumental to the enormous performance gains recently achieved in propositional SAT solving carry over smoothly to the rich domain of nonlinear Arithmetic Constraints. As a consequence, our approach is able to handle large Constraint systems with extremely complex Boolean structure, involving Boolean combinations of multiple thousand Arithmetic Constraints over some thousands of variables.

Christian Herde - One of the best experts on this subject based on the ideXlab platform.

  • Efficient Solving of Large Arithmetic Constraint Systems with Complex Boolean Structure: Proof Engines for the Analysis of Hybrid Discrete-Continuous Systems
    2011
    Co-Authors: Christian Herde
    Abstract:

    Hybrid Dynamical Systems Extending DPLL for Pseudo-Boolean Constraints Integration of DPLL-SAT and Linear Programming Integration of DPLL and Interval Constraint Solving

  • sat modulo ode a direct sat approach to hybrid systems
    Automated Technology for Verification and Analysis, 2008
    Co-Authors: Andreas Eggers, Martin Franzle, Christian Herde
    Abstract:

    In order to facilitate automated reasoning about large Boolean combinations of non-linear Arithmetic Constraints involving ordinary differential equations (ODEs), we provide a seamless integration of safe numeric overapproximation of initial-value problems into a SAT-modulo-theory (SMT) approach to interval-based Arithmetic Constraint solving. Interval-based safe numeric approximation of ODEs is used as an interval contractor being able to narrow candidate sets in phase space in both temporal directions: post-images of ODEs (i.e., sets of states reachable from a set of initial values) are narrowed based on partial information about the initial values and, vice versa, pre-images are narrowed based on partial knowledge about post-sets. In contrast to the related CLP(F) approach of Hickey and Wittenberg [12], we do (a) support coordinate transformations mitigating the wrapping effect encountered upon iterating interval-based overapproximations of reachable state sets and (b) embed the approach into an SMT framework, thus accelerating the solving process through the algorithmic enhancements of recent SAT solving technology.

  • efficient solving of large non linear Arithmetic Constraint systems with complex boolean structure
    Journal on Satisfiability Boolean Modeling and Computation, 2007
    Co-Authors: Martin Franzle, Christian Herde, Tino Teige, Stefan Ratschan, Tobias Schubert
    Abstract:

    In order to facilitate automated reasoning about large Boolean combinations of nonlinear Arithmetic Constraints involving transcendental functions, we provide a tight integration of recent SAT solving techniques with interval-based Arithmetic Constraint solving. Our approach deviates substantially from lazy theorem proving approaches in that it directly controls Arithmetic Constraint propagation from the SAT solver rather than delegating Arithmetic decisions to a subordinate solver. Through this tight integration, all the algorithmic enhancements that were instrumental to the enormous performance gains recently achieved in propositional SAT solving carry over smoothly to the rich domain of nonlinear Arithmetic Constraints. As a consequence, our approach is able to handle large Constraint systems with extremely complex Boolean structure, involving Boolean combinations of multiple thousand Arithmetic Constraints over some thousands of variables.

  • ecien t solving of large non linear Arithmetic Constraint systems with complex boolean structure
    2007
    Co-Authors: Christian Herde, Tino Teige, Stefan Ratschan, Tobias Schubert
    Abstract:

    In order to facilitate automated reasoning about large Boolean combinations of nonlinear Arithmetic Constraints involving transcendental functions, we provide a tight integration of recent SAT solving techniques with interval-based Arithmetic Constraint solving. Our approach deviates substantially from lazy theorem proving approaches in that it directly controls Arithmetic Constraint propagation from the SAT solver rather than delegating Arithmetic decisions to a subordinate solver. Through this tight integration, all the algorithmic enhancements that were instrumental to the enormous performance gains recently achieved in propositional SAT solving carry over smoothly to the rich domain of non-linear Arithmetic Constraints. As a consequence, our approach is able to handle large Constraint systems with extremely complex Boolean structure, involving Boolean combinations of multiple thousand Arithmetic Constraints over some thousands of variables.

Tino Teige - One of the best experts on this subject based on the ideXlab platform.

  • proofs of unsatisfiability for mixed boolean and non linear Arithmetic Constraint formulae
    MBMV, 2009
    Co-Authors: Stefan Kupferschmid, Tino Teige, Bernd Becker, Martin Franzle
    Abstract:

    Symbolic methods in computer-aided verification rely on appropriate Constraint solvers. Correctness and reliability of solvers are a vital requirement in the analysis of safety-critical systems, e.g., in the automotive context. Satisfiability results of a solver can usually be checked by probing the computed solution. However, efficient validation of an uncertified unsatisfiability result for some Constraint formula is nearly impossible. In this paper, we propose a certification method for unsatisfiability results for mixed Boolean and non-linear Arithmetic Constraint formulae. Such formulae arise in the analysis of hybrid discrete-continuous systems.

  • stochastic satisfiability modulo theory a novel technique for the analysis of probabilistic hybrid systems
    ACM International Conference Hybrid Systems: Computation and Control, 2008
    Co-Authors: Martin Franzle, Holger Hermanns, Tino Teige
    Abstract:

    The analysis of hybrid systems exhibiting probabilistic behaviour is notoriously difficult. To enable mechanised analysis of such systems, we extend the reasoning power of Arithmetic satisfiability-modulo-theory solving (SMT) by a comprehensive treatment of randomized (a.k.a. stochastic) quantification over discrete variables within the mixed Boolean-Arithmetic Constraint system. This provides the technological basis for a fully symbolic analysis of probabilistic hybrid automata. Generalizing SMT-based bounded model-checking of hybrid automata [2,11], stochastic SMT permits the direct and fully symbolic analysis of probabilistic bounded reachability problems of probabilistic hybrid automata without resorting to approximation by intermediate finite-state abstractions.

  • efficient solving of large non linear Arithmetic Constraint systems with complex boolean structure
    Journal on Satisfiability Boolean Modeling and Computation, 2007
    Co-Authors: Martin Franzle, Christian Herde, Tino Teige, Stefan Ratschan, Tobias Schubert
    Abstract:

    In order to facilitate automated reasoning about large Boolean combinations of nonlinear Arithmetic Constraints involving transcendental functions, we provide a tight integration of recent SAT solving techniques with interval-based Arithmetic Constraint solving. Our approach deviates substantially from lazy theorem proving approaches in that it directly controls Arithmetic Constraint propagation from the SAT solver rather than delegating Arithmetic decisions to a subordinate solver. Through this tight integration, all the algorithmic enhancements that were instrumental to the enormous performance gains recently achieved in propositional SAT solving carry over smoothly to the rich domain of nonlinear Arithmetic Constraints. As a consequence, our approach is able to handle large Constraint systems with extremely complex Boolean structure, involving Boolean combinations of multiple thousand Arithmetic Constraints over some thousands of variables.

  • ecien t solving of large non linear Arithmetic Constraint systems with complex boolean structure
    2007
    Co-Authors: Christian Herde, Tino Teige, Stefan Ratschan, Tobias Schubert
    Abstract:

    In order to facilitate automated reasoning about large Boolean combinations of nonlinear Arithmetic Constraints involving transcendental functions, we provide a tight integration of recent SAT solving techniques with interval-based Arithmetic Constraint solving. Our approach deviates substantially from lazy theorem proving approaches in that it directly controls Arithmetic Constraint propagation from the SAT solver rather than delegating Arithmetic decisions to a subordinate solver. Through this tight integration, all the algorithmic enhancements that were instrumental to the enormous performance gains recently achieved in propositional SAT solving carry over smoothly to the rich domain of non-linear Arithmetic Constraints. As a consequence, our approach is able to handle large Constraint systems with extremely complex Boolean structure, involving Boolean combinations of multiple thousand Arithmetic Constraints over some thousands of variables.