Hopfield associative memory on mesh

Abstract

The associative Hopfield memory is a very useful artificial neural network (ANN) that can be utilized in numerous applications. Examples include pattern recognition, noise removal, information retrieval, and combinatorial optimization problems. This paper provides an algorithm for implementing the Hopfield ANN on mesh parallel architectures. A Hopfield ANN model involves two major operations; broadcasting a value to a set of processors and summation of values in a set of processors. The main advantage of this algorithm is a high performance and cost effectiveness. An iteration of an N-bit (neuron) Hopfield associative memory only requires O(logN) time, whereas other known algorithms in literature of similar topology require O(N) time. Moreover, the proposed algorithm is cost effective because only higher dimension architectures were reported to achieve a complexity of O(logN) such as hypercubes.