A novel, to our knowledge, type of packet scheduler that could significantly outperform current state-of-the-art schedulers is presented. The operation and the design of such a scheduler are discussed, and a fully operational experimental implementation is described. The scheduler uses a neural network in a winner-take-all strategy to optimize decisions on the throughput of both a crossbar and a banyan switching fabric. The problems of high interconnection density are solved by use of a free-space optical interconnect that exploits diffractive optical techniques to generate the required interconnection patterns and weights.
An optoelectronic neural network is presented that is designed to solve the assignment problem--or any similar optimization task given minimal adjustment--in both crossbar and banyan packet switches. We examine the design decisions made at the hardware, software, and algorithmic levels and indicate the associated effect on the system as a whole. Clearly detailed experimental results show the system's robustness and performance due to the particular optoelectronic-algorithm combination used. The integration and packaging of such a system are also briefly discussed.
We examine a novel combination of architecture and algorithm for a packet switch controller that incorporates an experimentally implemented optically interconnected neural network. The network performs scheduling decisions based on incoming packet requests and priorities. We show how and why, by means of simulation, the move from a continuous to a discrete algorithm has improved both network performance and scalability. The system's limitations are examined and conclusions drawn as to its maximum scalability and throughput based on today's technologies.
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