Programmable, modular CNN cell

Drahoslav Lim, George S. Moschytz

Research output: Contribution to conferencePaperpeer-review

5 Scopus citations

Abstract

An experimental monolithic implementation of a programmable cellular neural network (CNN) is reported. It overcomes some of the characteristics and restrictions inherent in CMOS VLSI technologies, and allows an arbitrarily large continuous-time analog CNN to be built up by modularly connecting CNN chips with a modest number of cells. The template values are step-wise programmable, with values chosen for functionality rather than according to conventional binary weighting. All external input, output and control signals are electrical and digital, so the CNN can be directly connected to a controller. The design was carried out in a 1-micron n-well CMOS technology. Each cell occupies 0.4mm2, including all support circuitry; only one cell per chip was integrated in order to facilitate circuit testing. Measured CNN transients from a prototype 4×4 CNN, formed by connecting 16 one-cell chips are shown. The principal intended applications are the processing of acoustical signals and algorithm development.

Original languageEnglish
Pages79-84
Number of pages6
StatePublished - 1994
Externally publishedYes
EventProceedings of the 3rd IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94) - Rome, Italy
Duration: 18 Dec 199421 Dec 1994

Conference

ConferenceProceedings of the 3rd IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)
CityRome, Italy
Period18/12/9421/12/94

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