A Neural Network Design for Circuit Partitioning

This paper proposes a neural network model for circuit bipartitioning. The massive parallelism of neural nets has been successfully exploited to balance the partitions of a circuit and to reduce the external wiring between the partitions. The experimental results obtained by neural nets are found to be comparable with that achieved by Fiduccia and Mattheyses algorithm.