Genetic Fuzzy c-mean clustering-based decomposition for low power FSM synthesis

Most published results show that power reduction of the finite-state machines (FSMs) is achieved by decomposition. In order to achieve a low power FSM implementation, a Genetic Fuzzy c-mean clustering-based decomposition method, called GFCM-D, is proposed for FSM partition in this study. GFCM-D used Fuzzy c-mean clustering (FCM) to partition a set of states of FSM into a collection of c fuzzy clusters, then a FSM is decomposed into several sub machines. For achieving low power, the objective function of GFCM-D is to minimize the cross state transition probability between sub machines and increase the inner state transition probability within the submachine. Genetic algorithm (GA) is used as a shell, which applies selection, crossover and mutation for generating better centers and more appropriate clusters. We have tested our approach, GFCM-D, extensively on fifteen benchmarks, comparing it with previous FSM synthesis methods from various aspects. The experimental results show that GFCM-D has achieved a significant cost reduction of both dynamic power and leakage power dissipation over the previous publications.