Efficient Training of Neural Gas Vector Quantizers with Analog
Circuit Implementation

Stefano Rovetta, Member, IEEE, and Rodolfo Zunino, Member, IEEE



Abstract

This paper presents an algorithm for training vector quantizers
with an improved version of the Neural Gas model, and its
implementation in analog circuitry. Theoretical properties
of the algorithm are proven that clarify the performance of the
method in terms of quantization quality, and motivate design
aspects of the hardware implementation. The architecture for
vector quantization training includes two chips, one for Euclidean
distance computation, the other for programmable sorting of
codevectors. Experimental results obtained in a real application
(image coding) support both the algorithm’s effectiveness and the
hardware performance, which can speed up the training process
by up to two orders of magnitude.



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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS—II: ANALOG AND
DIGITAL SIGNAL PROCESSING, VOL. 46, NO. 6, JUNE 1999, pp.688-698