Enhancing sequential depth computation with a branch-and-bound algorithm

Abstract

We present an effective algorithm to enhance sequential depth computation. The sequential depth plays the most crucial role to the completeness of bounded model checking. Previous work computes the sequential depth by exhaustively searching the state space, which is unable to keep pace with the exponential growth of design complexity. To improve the computation, we develop an efficient approach that takes the branch-and-bound manner. We reduce the search space by applying a partitioning as well as a pruning method. Furthermore, we propose a novel formulation and integrate the techniques of BDDs and SAT solvers to search states that determine the sequential depth. Experimental results show that our approach considerably enhances the performance compared with the results of the previous work.