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A Self-Learning Analog Neural Processor

Gian Marco BO, Daniele D. CAVIGLIA, Maurizio VALLE

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Summary :

In this paper we present the analog architecture and the implementation of an on-chip learning Multi Layer Perceptron network. The learning algorithm is based on Back Propagation but it exhibits increased capabilities due to local learning rate management. A prototype chip (SLANP, Self-Learning Neural Processor) has been designed and fabricated in a CMOS 0.7 µm minimum channel length technology. We report the experimental results that confirm the functionality of the chip and the soundness of the approach. The SLANP performance compare favourably with those reported in the literature.

Publication
IEICE TRANSACTIONS on Fundamentals Vol.E85-A No.9 pp.2149-2158
Publication Date
2002/09/01
Publicized
Online ISSN
DOI
Type of Manuscript
PAPER
Category
Neural Networks and Bioengineering

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