A State Assignment Method for Extended Burst-Mode gC Finite State Machines Based on Genetic Algorithm

This paper proposes a new algorithm for state assignment of Extended Burst-Mode Asynchronous Finite State Machines (XBM_AFSM). The proposal is based on genetic algorithm and it introduces a novel style of state assignment. It improves the results and it overcomes the previous methods found in literature once it addresses the "state minimization", the "critical race free coding" and "coverage" as a single problem. Furthermore, it is able to detect the conflicts in XBM specification and to insert the minimum number of state variables in the XBM specification in order to eliminate those conflicts. A dedicated computational tool called SAGAAs_gC implemented the algorithm and it was tested in a set of 39 XBM benchmarks. When it is compared to 3D tool, our method achieved an average reduction of 21.4%, 16.5% and 12.12% in amount of state variables, number of literals and transistors, respectively. Results show that the method and dedicated computational tool SAGAAs_gC achieved good and reliable results, showing a high potential of practical implementation in actual circuit designs.