Pipelining and parallel training of neural networks on distributed-memory multiprocessors

This paper presents a parallel neural network simulator, implemented on a Parsytec Multicluster2 transputer system. In practical use, neural networks often employ the backpropagation learning rule, as this supervised learning method can be applied to a wide field of recognition problems. The authors focus on the acceleration of backpropagation learning by combining pipelining and parallel training methods. The pipelining model was proposed by Klauer (1992), which actually is independent of the parallel hardware used. This contribution continues the idea of concurrency and pipelining by a concrete implementation