Comparison of parallel and serial implementation of feedforward neural networks

Abstract

In this paper we investigate parallel implementation of feedforward neural networks on a transputer array when the networks are trained by the backpropagation algorithm. Two methods of transputer implementations are considered: namely, the processor farming method and the pipelining method. The performance of the parallel implementations are compared with a serial implementation on a 486 machine using the N-X-N encoder/decoder as the benchmark problem. Simulation results show that for small networks serial implementation out-performs the parallel implementations, but, as the network and training set size becomes large, parallel implementations produce shorter training times than the serial implementation. Among the two methods of transputer implementations, the pipelining method always produced shorter training times than the processor farming method.