Parallelizing Feed-Forward Artificial Neural Networks on Transputers

Abstract

This thesis is about parallelizing the training phase of a feed-forward, artificial neural network. More specifically, we develop and analyze a number of parallelizations of the widely used neural net learning algorithm called back-propagation.

We describe two different strategies for parallelizing the back-propagation algorithm. A number of parallelizations employing these strategies have been implemented on a system of 48 transputers, permitting us to evaluate and analyze their performances based on the results of actual runs.