Exploiting symbolic techniques within genetic algorithms for power optimization

Proposes an optimization algorithm for reducing the power dissipation in a sequential circuit. The encoding of the different states in a finite-state machine is modified to obtain a functionally equivalent circuit that exhibits a reduced power dissipation. The algorithm is based on a newly-proposed power estimation function that is able to quickly give an accurate estimate of the dissipated power without actually synthesizing the circuit. Given this estimate, a genetic algorithm provides a state re-encoding for the circuit. The estimation function is computed in a very efficient way by exploiting some symbolic computations with binary decision diagrams. The algorithm is experimentally shown to provide good results from the power optimization point of view, at a limited cost in terms of area increase, when compared with similar approaches