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The development of neural network models requires the study of dedicated hardware architectures. In this paper, we propose on implementation of Radial Basis Function networks, derive an architecture based on an already existing 2D-systolic machine (MANTRA). A systolic algorithm is described to implement the required functions and the suitable sequence of operations. Theoretical efficiencies are estimated on the key tasks and some guidelines are given for a best usage of the Mantra machine in the studied framework...
In this paper we compare the implementations of Radial Basis Function (RBF) Neural Network on three parallel Neuro-Computers: the DRA machine (1D), the SMART machine (1D) and the MANTRA machine (2D). RBF networks can be used as probability density function estimators in a classification framework. The amount of calculation required for the simulation of such networks grows rapidly with the size of 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 of 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 limitations for real time classification. Finally, we consider two real data-bases examples on which we compare the different machines...