Temperature perturbation effects on image processing dedicated stochastic artificial neural networks
Kurosh Madani, Ion Berechet · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
The implementation of artificial neural networks (ANN) as CMOS analog integrated circuits shows several attractive features. Stochastic models, especially the Boltzmann Machine, show a number of many attractive features. Recent studies on artificial models point out that classification is their most successful application field, and that real pattern recognition tasks, and especially image processing by artificial neural networks will require large networks. All of the presented implementations of ANN are supposed to be working in ideal conditions but real applications are subject to perturbations. For a digital implementation of ANN perturbation effects could be neglected in a firth 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 Boltzmann Machine model's behavior with physical temperature perturbation. The relation between the T parameter of the Boltzmann Machine model and the physical temperature of circuit has been established. Simulation results are presented and temperature effects compensation is discussed.