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22 March 1996 Perturbation effects analysis in analog implementation of a stochastic artificial neural network
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Analogue implementation of Artificial Neural Networks (ANN) especially as CMOS integrated circuits show several attractive features. During the last decade, numerous works show that small size analogue ANN operate correctly. However, today the efforts are focused on real industrial size application of ANN that 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 be subject to some global perturbations. Especially in the case of the analogue and mixed digital/analogue implementation, the behavior analysis of the neural network with perturbation conditions is thus inevitable. Unfortunately, very few papers analyze the behavior of analogue neural network with global perturbations. We have investigated modeling and experimental validation of the behavior of analogue ANN in the case of a global perturbation of the network. We have analyzed the behavior of a CMOS analogue implementation of synchronous Boltzmann Machine model when the neural circuit is subject to perturbations. The perturbations we have considered concern the supply voltage of the neural circuit and ambient temperature in which the circuit operates. In this paper we present the analysis of the behavior of the analogue implementation of synchronous Boltzmann Machine with electrical and thermal perturbations. Simulation and experimental results have been exposed.
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Kurosh Madani and Ghislain de Tremiolles "Perturbation effects analysis in analog implementation of a stochastic artificial neural network", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996);

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