MUSTANG: state assignment of finite state machines targeting multilevel logic implementations

摘要

The problem of state assignment for synchronous finite-state machines (FSM), targeted towards multilevel combinational logic and feedback register implementations, are addressed. The authors present state-assignment algorithms that heuristically maximize the number of common cubes in the encoded network to maximize the number of literals in the resulting combinational logic network after multilevel logic optimization. Results over a wide range of benchmarks which prove the efficacy of the proposed techniques are presented. Literal counts averaging 20%-40% less than other state-assignment programs have been obtained.