An analog neural network processor for self-organizing mapping

Bing J. Sheu, Joongho Choi, Chia Fen Chang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

The building blocks of a self-organizing analog neural chip are shown. Its function is to evaluate the large number of dot products of the given input vectors and the stored weight vectors in a fully parallel format. Lateral competition is to be performed among the analog output voltages and the neural unit with the largest voltage level is to emerge as a single winner. Updating synapse weights is performed in a digital signal processor using an unsupervised learning rule. Design considerations addressed in the construction of the WTA (winner-take-all) circuit are: high resolution, fast operation, and layout compactness. The 4-MHz analog neural network processor chip, fabricated in a 2μm CMOS process, contains 25 neurons in the input layer and 64 neurons in the competitive layer. The behavior of the WTA circuit with only one winner for a lossy image data compression application is shown.

Original languageEnglish
Title of host publicationDigest Technical Papers - 1992 39th IEEE International Solid-State Circuits Conference, ISSCC 1992
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages136-137
Number of pages2
ISBN (Electronic)0780305736
DOIs
StatePublished - 1992
Event39th IEEE International Solid-State Circuits Conference, ISSCC 1992 - San Francisco, United States
Duration: 19 Feb 199221 Feb 1992

Publication series

NameDigest of Technical Papers - IEEE International Solid-State Circuits Conference
Volume1992-February
ISSN (Print)0193-6530

Conference

Conference39th IEEE International Solid-State Circuits Conference, ISSCC 1992
Country/TerritoryUnited States
CitySan Francisco
Period19/02/9221/02/92

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