A VLSI Architecture for Fast Clustering with Fuzzy ART Neural Networks

Authors:
E. Granger;Y. Savaria;Y. Blaquière;M.-A. Cantin;P. Lavoie
Affiliations:
-;-;-;-;-
Venue:
NICROSP '96 Proceedings of the 1996 International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing (NICROSP '96)
Year:
1996

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A high-performance VLSI architecture for the histogram peak-climbing data clustering algorithm

IEEE Transactions on Very Large Scale Integration (VLSI) Systems

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Abstract

The hardware implementation of the Fuzzy ART neural network applied to a demanding real time radar signal clustering problem is investigated. To obtain efficient solutions for implementing this neural network with dedicated hardware, the network's algorithm is reformulated, and then a novel Fuzzy ART system architecture is proposed. This system architecture is composed of a global comparator and several identical elementary modules (EMs), each one emulating a number of neurons. The general architecture of each EM consists of a local comparator, dividers, neural processors, and a block of memory.