Integrated area-power optimal state assignment

This paper presents a state assignment algorithm with the objective of lowe r energy along with area comparable to the area-targeting state assignments such as JEDI. The underlying framework is MUSTANG's complete weighted grap h with weights representing state affinity, The weight computation phase es timates the computation energy of potential common cubes using steady state probabilities for transitions, The weight computation phase also identifie s a large set of potential state cliques, which are incorporated into a rec ursive bipartitioning based state assignment procedure. Reuse of cliques id entified by the weight computation phase results in a faster and efficient state assignment. The energy targeting weights result in approximate to9% l ower area and 18% lower power than area targeting weights in JEDI over 29 M CNC Logic Synthesis '93 benchmarks. The clique based state assignment perfo rms almost as well as the annealing based state assignment in JEDI, and tak es only about half as much time.