Simplified neural architectures for symmetric boolean functions

Bernard Girau 1
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : The theoretical and practical framework of Field Programmable Neural Arrays has been defined to reconcile simple hardware topologies with complex neural architectures: FPNAs lead to powerful neural models whose original data exchange scheme allows to use hardware-friendly neural topologies. This paper addresses preliminary results in the study of the computation power of FPNAs. The computation of symmetric boolean functions is taken as a textbook example. The FPNA concept allows successive topology simplifications of standard neural models for such functions, so that the number of weights is greatly reduced with respect to previous works.
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Communication dans un congrès
European Symposium on Artificial Neural Networks, 2000, none, 2000
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https://hal.inria.fr/inria-00099318
Contributeur : Publications Loria <>
Soumis le : mardi 26 septembre 2006 - 08:52:44
Dernière modification le : jeudi 11 janvier 2018 - 06:19:48

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  • HAL Id : inria-00099318, version 1

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Bernard Girau. Simplified neural architectures for symmetric boolean functions. European Symposium on Artificial Neural Networks, 2000, none, 2000. 〈inria-00099318〉

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