A Self-Learning Analog Neural Processor

Gian Marco BO  Daniele D. CAVIGLIA  Maurizio VALLE  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E85-A   No.9   pp.2149-2158
Publication Date: 2002/09/01
Online ISSN: 
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Neural Networks and Bioengineering
neural network,  multilayer perceptron,  gradient descent learning,  analog VLSI,  on-chip learning,  

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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.