Analog VLSI Circuits for Competitive Learning Networks

Authors:
H. C. Card;D. K. McNeill;C. R. Schneider
Affiliations:
Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, Canada R3T 5V6;Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, Canada R3T 5V6;Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, Canada R3T 5V6
Venue:
Analog Integrated Circuits and Signal Processing - Special issue: cellular neural networks and analog VLSI
Year:
1998

Citing 25
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Abstract

An investigation is made concerning implementations of competitivelearning algorithms in analog VLSI circuits and systems. Analog and lowpower digital circuits for competitive learning are currently important fortheir applications in computationally-efficient speech and image compressionby vector quantization, as required for example in portable multi-mediaterminals. A summary of competitive learning models is presented to indicatethe type of VLSI computations required, and the effects of weightquantization are discussed. Analog circuit representations of computationalprimitives for learning and evaluation of distortion metrics are discussed.The present state of VLSI implementations of hard and soft competitivelearning algorithms are described, as well as those for topological featuremaps. Tolerance of learning algorithms to observed analog circuit propertiesis reported. New results are also presented from simulations offrequency-sensitive and soft competitive learning concerning sensitivity ofthese algorithms to precision in VLSI learning computations. Applications ofthese learning algorithms to unsupervised feature extraction and to vectorquantization of speech and images are also described.