Optimized Cellular Neural Network Universal Machine emulation on FPGA

Giovanni Egidio Pazienza, Jordi Bellana-Camañes, Jordi Riera-Baburés, Xavier Vilasís-Cardona, Marco Antonio Moreno-Armendáriz, Marco Balsi

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

3 Scopus citations

Abstract

An FPGA architecture to emulate a single-layer Cellular Neural Network - Universal Machine (CNN-UM) is proposed. It is based on a fast realization of the CNN convolution operation on the parallel hardware of the FPGA. The setup is capable of performing a CNN iteration over a 30×30 pixel image in less than 30 μs. Moreover, this platform has been used to realize the visual system of an autonomous mobile robot.

Original languageEnglish
Title of host publicationEuropean Conference on Circuit Theory and Design 2007, ECCTD 2007
PublisherIEEE Computer Society
Pages815-818
Number of pages4
ISBN (Print)1424413427, 9781424413423
DOIs
StatePublished - 2007
EventEuropean Conference on Circuit Theory and Design 2007, ECCTD 2007 - Seville, Spain
Duration: 26 Aug 200730 Aug 2007

Publication series

NameEuropean Conference on Circuit Theory and Design 2007, ECCTD 2007

Conference

ConferenceEuropean Conference on Circuit Theory and Design 2007, ECCTD 2007
Country/TerritorySpain
CitySeville
Period26/08/0730/08/07

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