This paper presents algorithms for approximate finite state machine traversal based on state space decomposition. The original finite state machine is partitioned in component submachines, and each of them is traversed separately; the result of the computation is an over-estimation of the set of reachable states of the original machine. Different traversal strategies, which reduce the effects of the degrees of freedom introduced by the decomposition, are discussed. Efficient partitioning is a key point for the performance of the traversal techniques; a method to heuristically find a good decomposition of the overall finite state machine, based on the exploration of its state variable dependency graph, is proposed. Applications of the approximate traversal methods to logic optimization of sequential circuits and behavioral verification of finite state machines are described; experimental results for such applications, together with data concerning pure traversal, are reported.
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