A binary Hopfield neural network with hysteresis for large crossbar packet-switches

In this paper, we propose a hysteretic Hopfield neural network architecture for efficiently solving crossbar switch problems. A binary Hopfield neural network architecture with hysteresis binary neurons and its collective computational properties are studied. The network architecture is applied to a crossbar switch problem and results of computer simulations are presented and used to illustrate the computation power of the network architecture.