PARALLEL PERCEPTRON LEARNING ON A SINGLE-CHANNEL BROADCAST COMMUNICATION MODEL

Abstract

A parallel perceptron learning algorithm based upon a single-channel broadcast communication model has been proposed here. Since it can process training instances in parallel, instead of one by one in the conventional algorithm, large speedup can be expected. Theoretical analysis shows: with n processors, the average speedup ranges from O(log n) to O(n) under a variety ot assumptions (where n is the number of training instances). Experimental results further show the actual average speedup is approximately being O(n0.91/log n). Extensions to a bounded number of processors and to the backpropagation learning have also been discussed.