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Design of a Third Generation Real-Time Cellular Neural Network Emulator

dc.contributor.authorYildiz, Nerhun
dc.contributor.authorCesur, Evren
dc.contributor.authorTavsanoglu, Vedat
dc.date.accessioned2026-06-27T13:37:49Z
dc.date.issued2014
dc.description.abstractIn this paper, the features of the next generation Real-Time Cellular Neural Network Processor (RTCNNP-v3) are discussed. The RTCNNP-v2 structure is the only CNN implementation that is reported to be capable of processing full-HD 1080p@60 (1920 x 1080 resolution at 60 Hz frame rate) video images in real-time, due to its fully-pipelined architecture, however, it has some weaknesses like the inability to divide the processing in spatial domain, record and recall intermediate results to an external memory and has some issues in its internal memory coding. Those shortcomings are to be addressed in the next design of our CNN emulator - RTCNNP-v3, which will increase the range of applications and enable the implementation to match the requirements of the cutting-edge movie production technologies like UHD (4K) and the future FUHD (8K).en
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TOBITAK) [108E023]
dc.identifier.isbn978-1-4799-6007-1
dc.identifier.issn2165-0179
dc.identifier.urihttps://hdl.handle.net/20.500.14981/53930
dc.identifier.wos000346574800031
dc.language.isoeng
dc.publisherIEEE
dc.relation.conference14th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA)
dc.relation.ispartof2014 14TH INTERNATIONAL WORKSHOP ON CELLULAR NANOSCALE NETWORKS AND THEIR APPLICATIONS (CNNA)
dc.subjectComputer Science
dc.subjectScience & Technology - Other Topics
dc.titleDesign of a Third Generation Real-Time Cellular Neural Network Emulator
dc.typeProceedings Paper
dspace.entity.typePublication
local.import.sourceWOS

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