Neural network architecture for crossbar switch control

A Hopfield neural network architecture for the real-time control of a crossbar switch for switching pockets at maximum throughput is proposed. The network performance and processing time are derived from a numerical simulation of the transitions of the neural network. A method is proposed to optimize electronic component parameters and synaptic connections, and it is fully illustrated by the computer simulation of a VLSI implementation of 4×4 neural net controller. The extension to larger size crossbars is demonstrated through the simulation of an 8×8 crossbar switch controller, where the performance of the neural computation is discussed in relation to electronic noise and inhomogeneities of network components