Symbolic optimization of FSM networks based on sequential ATPG techniques

Abstract

This paper presents a novel optimization algorithm for FSM networks that relies on sequential test generation and redundancy removal. The implementation of the proposed approach, which is based on the exploitation of input don't care sequences through regular language intersection, is fully symbolic. Experimental results, obtained on a large set of standard benchmarks, improve over the ones of state-of-the-art methods.