Designing parallel computers for self organizing maps

Abstract

Self organizing maps (SOM) are a class of artificial neural network (ANN) models developed by Kohonen. There are a number of variants, where the self organizing feature map (SOFM) is one of the most used ANN models with unsupervised learning. Learning vector quantifiers (LVQ) is another group of SOM which can be used as very efficient classifiers. SOM have been used in a variety of fields, e.g. robotics, telecommunication and speech recognition. Currently there is a great interest in using parallel computers for ANN models. In this report we describe different ways to implement SOM on parallel computers. We study the design of massively parallel computers, especially computers with simple processing elements, used for SOM calculations. It is found that SOM (like many other ANN models) demands very little of a parallel computer. If support for broadcast and multiplication is included very good performance can be achieved on otherwise modest hardware.