On state assignment of finite state machines using hypercube embedding approach

We address the problem of state assignment of finite state machines (FSMs). The approach used by us to solve the state assignment problem is based on hypercube embedding. We have designed a new technique to efficiently solve the hypercube embedding problem by integrating two different techniques; one of these is the gradient projection method while the other is a variant of the Kernighan-Lin algorithm. The gradient projection method operates in continuous space and improves an initial feasible solution iteratively by tracing a search path in gradient descent direction. The Kernighan-Lin algorithm operates in discrete space and also improves an initial feasible solution iteratively. We have integrated both techniques in such a way that output from the gradient projection method is fed to the Kernighan-Lin style algorithm. The effectiveness of the proposed technique is shown by comparing its results with another technique on a number of MCNC benchmark examples for logic synthesis and optimization