A neural network model for traffic controls in multistage interconnection networks

Summary form only given. A neural network model for data traffic controls in multistage interconnection networks is discussed. The goal of the neural network model is to find conflict-free traffic flows to be transmitted among given I/O traffic demands in order to maximize the network throughput. The model requires n² processing elements for the traffic control in an n×n multistage interconnection network. The model runs not only on a sequential machine but also on a parallel machine with maximally n ² processors. The model was verified by solving ten 32×32 network problems