Design of a third generation Real-Time Cellular Neural Network emulator

Author

Yildiz, Nerhun ; Cesur, Evren ; Tavsanoglu, Vedat

Author_Institution

Electron. & Commun. Eng., Yildiz Tech. Univ., Istanbul, Turkey

fYear

2014

fDate

29-31 July 2014

Firstpage

1

Lastpage

2

Abstract

In 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 (1920×1080 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).

Keywords

cellular neural nets; image resolution; next generation networks; pipeline processing; real-time systems; video signal processing; CNN implementation; FUHD; RTCNNP-v2 structure; RTCNNP-v3 structure; external memory; full-HD video image processing; internal memory coding; movie production technologies; next generation real-time cellular neural network processor; pipelined architecture; spatial domain; third-generation real-time cellular neural network emulator design; Arrays; Cellular neural networks; Computed tomography; Educational institutions; Field programmable gate arrays; Next generation networking;

fLanguage

English

Publisher

ieee

Conference_Titel

Cellular Nanoscale Networks and their Applications (CNNA), 2014 14th International Workshop on

Conference_Location

Notre Dame, IN

Type

conf

DOI

10.1109/CNNA.2014.6888621

Filename

6888621