Architecture of a fully pipelined real-time cellular neural network emulatort
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Dosyalar
Tarih
2015-01
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE-INST Electrical Electronics Engineers Inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this paper, architecture of a Real-Time Cellular Neural Network (CNN) Processor (RTCNNP-v2) is given and the implementation results are discussed. The proposed architecture has a fully pipelined structure, capable of processing full-HD 1080p@60 (1920 1080 resolution at 60 Hz frame rate, 124.4 MHz visible pixel rate) video streams, which is implemented on both high-end and low-cost FPGA devices, Altera Stratix IV GX 230, and Cyclone III C 25, respectively. Many features of the architecture are designed to be either pre-synthesis configurable or runtime programmable, which makes the processor extremely flexible, reusable, scalable, and practical.
Açıklama
Anahtar Kelimeler
Cellular neural networks, Field programmable gate arrays, Real time systems, Reconfigurable architectures
Kaynak
IEEE Transactions on Circuits and Systems I: Regular Papers
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
62
Sayı
1
Künye
Yıldız, N., Cesur, E., Kayaer, K., Tavşanogğu, A. V. & Alpay, M. (2015). Architecture of a fully pipelined real-time cellular neural network emulator. IEEE Transactions on Circuits and Systems I: Regular Papers, 62(1), 130-138. doi:10.1109/TCSI.2014.2345502
