One of the major issues in using artificial neural networks is reducing the training and the testing times. Parallel processing is the most efficient approach for this purpose. In this paper, we explore the parallel implementation of the backpropagation algorithm with and without hidden layers  on MasPar MP-I. This implementation is based on the SIMD architecture, and uses a backpropagation model which is more exact theoretically than the serial backpropagation model. This results in a smoother convergence to the solution. Most importantly, the processing time is reduced both theoretically and experimentally by the order of 3000, due to architectural and data parallelism of the backpropagation algorithm. This allows large-scale simulation of neural networks in near real-time.
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