A method for data path synthesis using neural networks

Presents a deterministic parallel algorithm to solve the data path allocation problem in high-level synthesis. The method is based on the modified Hopfield neural network model of computation and the McCulloch-Pitts binary neuron model. The proposed algorithm has a running time complexity of O(1) for a neural network with n vertices and c cliques. A sequential simulator was implemented for the proposed algorithm on a Linux Pentium PC under X Windows. Several circuits hare been attempted, all yielding sub-optimal solutions.