Decomposed Symbolic Forward Traversals of Large Finite State Machines

BDD-based symbolic traversals are the state-of-the-art technique for reachability analysis of Finite State Machines. They are currently limited to medium-small circuits for two reasons: BDD peak size during image computation and BDD explosion for state space representation. Starting from these limits, this paper presents a technique that decomposes the search space decreasing the BDD peak size and the number of page faults during image computation. Results of intermediate computations and large BDDs are efficiently stored in the secondary memory. A decomposed traversal that allows exact explorations of state spaces is obtained. Experimental results show that this approach is particularly effective on the larger MCNC, ISCAS'89, and ISCAS'89-addendum circuits