Probabilistic state space search

  • Authors:
  • Andreas Kuehlmann;Kenneth L. McMillan;Robert K. Brayton

  • Affiliations:
  • IBM T. J. Watson Research Center, Yorktown Heights, NY;Cadence Design Systems, Berkeley, CA;University of California at Berkeley, Berkeley, CA

  • Venue:
  • ICCAD '99 Proceedings of the 1999 IEEE/ACM international conference on Computer-aided design
  • Year:
  • 1999

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Abstract

This paper describes a probabilistic approach to state space search. The presented method applies a ranking of the design states according to their probability of reaching a given target state based on a random walk model. This ranking can be used to prioritize an explicit or partial symbolic state exploration to find a trajectory from a set of initial states to a set of target states. A symbolic technique for estimating the reachability probability is described which implements a smooth trade-off between accuracy and computing effort. The presented probabilistic state space search complements incomplete verification methods which are specialized in finding errors in large designs.