Comparing implementations of radial basis function neural networks on three parallel machines

Abstract

In this paper we compare the implementations o1 Radial Basis Function (RBF) Neural Network on three parallel Neuro-Camputers: the DRA machine (ID), the SMART machine (119} and the MANTRA machine (2D). RBF networks can be used as probability density]unction estimators in a classification. framework. The amount of calculation required.for the simulation of such networks grows rapidly with the size o1 the learning database. Due to the highly parallel nature of RBF networks, parallel architectures are ideal candidates for such simulations. In this work we have tried to make a comparison o] the three architectures based on the efficiency measure. We conclude this paper by outlining the different algorithmic constraints imposed by the particularities of each of the three architectures. We also discuss the I/O limitationsfor real time classification. Finally, we consider two real data-bases examples on which we compare the different machines.