On an Improved FPGA Implementation of CNN-Based Gabor-Type Filters

On an Improved FPGA Implementation of CNN-Based Gabor-Type Filters

In this brief, the details of the architecture of a previously introduced improved field-programmable gate array implementation of the cellular neural network (CNN)-based 2-D Gabor-type filter are given, and the implementation results are discussed. The proposed architecture is suitable for real-tim...

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Veröffentlicht in:IEEE transactions on circuits and systems. II, Express briefs Express briefs, 2012-11, Vol.59 (11), p.815-819
Hauptverfasser: Cesur, E., Yildiz, N., Tavsanoglu, V.
Format: Artikel
Sprache:eng
Schlagworte:
Architecture
Bandwidth
Cellular neural networks (CNNs)
Circuits
Computer architecture
Convergence
Equations
Field programmable gate arrays
field-programmable gate arrays (FPGAs)
Finite impulse response filter
Gabor filters
Neural networks
Pixels
Real time
real-time systems
reconfigurable architectures
Streams
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Beschreibung
Zusammenfassung:In this brief, the details of the architecture of a previously introduced improved field-programmable gate array implementation of the cellular neural network (CNN)-based 2-D Gabor-type filter are given, and the implementation results are discussed. The proposed architecture is suitable for real-time applications with high pixel rates. The prototype is capable of processing video streams up to a pixel rate of 373.2 megapixels per second (MP/s), including full-high-definition (HD) 1080p@60 (1080 × 1920 resolution, 60-Hz frame rate, and 124.4-MP/s visible pixel rate). This brief also contains convergence rate analysis results, along with some discussions on FIR and CNN-based implementation methods.
ISSN:1549-7747
1558-3791
DOI:10.1109/TCSII.2012.2218471