In this paper we present a new tool for the encoding of multi-level finite state machines based on the concept of
evolution programming. Evolution programs are stochastic adaptive algorithms, based on the paradigm of genetic
algorithms whose search methods model some natural phenomenon: genetic inheritance and Darwinian strife for
survival. Crossover and mutation rates were tailored to the state assignment problem experimentally. We present
results over a wide range of MCNC benchmarks which demonstrate the effectiveness of the new tool. The results
show that evolution programs can be effectively applied to state assignment.
In this paper, we study the problem of state assignment as it relates to silicon area, propagation delay time and
testability of finite state machines. The results of a study involving various FSM benchmarks show that the simple
technique of one-hot encoding often produces better results than those attained by complex state assignment
algorithms.
Pioneer: A New Tool for Coding of Multi-Level Finite
State Machines Based on Evolution Programming
