A VLSI-based Gaussian kernel mapper for real-time RBF neural networks

Seth Wolpert, M. J. Osborn, M. T. Musavi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

An analog VLSI circuit approach to a radial basis function (RBF) neural network is explored. For each of a number of reference pattern templates, the circuit calculates the Euclidean distance between that template and an unknown point, and maps each distance to a point on the Gaussian surface of that template. Then, these points may either be added in order to form the basis for an RBF approximator or laterally inhibited to form the basis for an RBF classifier. The circuitry for this network has been implemented in 2-micron CMOS technology, and will form the bases for truly parallel and simultaneous standalone neural networks that function in real time without intervention from conventional computers.

Original languageEnglish (US)
Title of host publicationProceedings of the 18th IEEE Annual Northeast Bioengineering Conference, NEBEC 1992
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages51-52
Number of pages2
ISBN (Electronic)0780309022
DOIs
StatePublished - Jan 1 1992
Event18th IEEE Annual Northeast Bioengineering Conference, NEBEC 1992 - Kingston, United States
Duration: Mar 12 1992Mar 13 1992

Publication series

NameProceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC
ISSN (Print)1071-121X
ISSN (Electronic)2160-7001

Conference

Conference18th IEEE Annual Northeast Bioengineering Conference, NEBEC 1992
Country/TerritoryUnited States
CityKingston
Period3/12/923/13/92

All Science Journal Classification (ASJC) codes

  • Bioengineering

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