Quadrisectioning based placement with a normalized mean field neural network

A quadrisectioning based neural network algorithm for the placement problem in VLSI layout synthesis is presented. The mean field theory neural network with graded neurons proposed by Peterson and Soderberg is used. It is renamed normalized mean field net. The problem is solved by recursive quadrisectioning where, at each step, all neurons in the network evolve simultaneously, maintaining a level of globality. In the authors' simulations, the network is able to find optimal solutions to all hand constructed test problems with up to 256 modules.<>