<title>Analysis of limitations in analogue implementation of stochastic artificial neural networks</title>

Abstract

The implementation of artificial neural networks (ANN) as CMOS analog integrated circuits shows several attractive features. Stochastic models, and especially the Boltzmann Machine shows a number of many attractive features. Numerous papers show that small size analog networks operate correctly. However, recent studies on artificial models point out that classification is their most successful application field: so real pattern recognition tasks will require large networks. On the other hand, all of the presented implementations of ANN have been supposed to be working in ideal conditions but real applications will subject to perturbations. For a digital implementation of ANN perturbation effects could be neglected in a fifth-order approximation. But for the analog and mixed digital/analog implementation cases, the behavior analysis of the neural network with perturbation conditions is inevitable. Unfortunately, very few papers analyze the behavior of analog neural networks with perturbation or their limitations. In this paper we present the analysis of a CMOS analog implementation of synchronous Boltzmann Machine model's behavior with physical temperature perturbations. The relation between the T parameter of the Boltzmann Machine's model and the physical temperature of circuit has been presented. Simulation results have been given, temperature effects compensation have been discussed, and experimental results have been exposed.