Neural Networks for Fast Arbitration and Switching Noise Reduction in Large Crossbars

A neural network-based controller is presented for the real-time arbitration of routing paths in large crossbar switches constructed from one-sided crosspoint chips. This controller is suitable for a synchronous environment where a number of connection-requests are presented simultaneously to the switch. The controller aims to maximize the effective bandwidth of the switch and to minimize the simultaneous-switching noise in the individual chips. The controller uses multiple winner-take-all networks coupled with some competitive-cooperative mechanisms to achieve the joint optimization. The effect of various network parameters are studied through simulation, and cases leading to non-optimal solutions analyzed. The results show that the arbitration complexity and time scale well with the size of the switches, and the throughput achieved is close to the theoretically maximum attainable. We also introduce a hierarchical neural network controller for a packet-switched environment where conne..