Circuit partitioning using parallel mean field annealing algorithms

Mean field annealing (MFA) algorithm, recently proposed for solving combinatorial optimization problems, combines the characteristics of neural networks and simulated annealing. Previous works on MFA resulted with successful mapping of the algorithm to some classic optimization problems such as travelling salesman problem and graph partitioning problem. In this paper, MFA is formulated for circuit partitioning problem (CPP) by using both graph and network models. Initial results of the implementations show that MFA can be used as an efficient alternative heuristic for CPP. MFA algorithms proposed for solving CPP are parallelized and implemented on an iPSC/2 hypercube multicomputer. Experimental results show that the proposed heuristics can be efficiently parallelized on hypercube multicomputers, which is crucial for algorithms solving such computationally hard problems