A VLSI systolic implementation of the Hopfield and back‐propagation neural algorithms

Abstract

Combining the parallel and neural paradigms seems, at first glance, to be a natural process, since it is a methodology derived from the part played by the biological and mathematical behavior of a neuron. It is proposed that any neural algorithm is inherently a parallel application. The structure of a neural algorithm and the function of a neuron suggest the choice of the systolic approach. However, interest should be restricted only to those well‐known neural models such as the Hopfield and back‐propagation neural networks. It is also shown that the systolic approach is best suited to the parallelization of the patterns training phase of the neural algorithms in terms of mapping the two structures (systolic and neural networks).