Neural networks for fast arbitration and switching noise reduction in large crossbars

A neural-network-based controller for the real-time arbitration of routing paths in large crossbar switches constructed from one-sided crosspoint chips is presented. This controller is suitable for a synchronous environment where a number of connection requests are simultaneously presented 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 effects of various network parameters are studied through simulation, and cases leading to nonoptimal solutions are analyzed. A hierarchical neural network controller for a packet-switched environment where connections are established and broken asynchronously is introduced. This controller provides almost the same level of performance as the first, but with significantly reduced computation for each connection request. >

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