An Emulated Digital Retina Model Implementation on FPGA

Nagy, Zoltán; Vörösházi, Zsolt; Szolgay, Péter

doi:10.1109/cnna.2005.1543215

Cited by 8 publications

(3 citation statements)

References 2 publications

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“…CNN templates are kept directly on the chip, and the images to process are partially stored on the FPGA memory according to an accurate trade-off between internal memory available and input throughput. The Falcon architecture has found application in a number of tasks, like the emulation of a digital retina model in real-time [7].…”

Section: State Of the Art Of Cnn-um Emulation On Fpgamentioning

confidence: 99%

Optimized cellular neural network universal machine emulation on FPGA

Pazienza

Bellana-Camanes

Riera-Babures

et al. 2007

2007 18th European Conference on Circuit Theory and Design

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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.

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Section: State Of the Art Of Cnn-um Emulation On Fpgamentioning

confidence: 99%

Optimized cellular neural network universal machine emulation on FPGA

Pazienza

Bellana-Camanes

Riera-Babures

et al. 2007

2007 18th European Conference on Circuit Theory and Design

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show abstract

“…These CNN-based retina models can be realized in different forms, such as software simulator, analog VLSI 2 EURASIP Journal on Advances in Signal Processing chips, and emulated-digital hardware (ASIC or FPGA) [6,7]. However, the determination of the model parameters requires very high computing power and accurate solution.…”

Section: Introductionmentioning

confidence: 99%

“…By using reconfigurable FPGAs, we could handle the inherently multilayer structure of a retina model, and the parameters and values can be represented with scalable accuracy [10,11]. The previously elaborated single-channel model [7] was extended to a multichannel one with higher hardware requirements and optimizations. The complete real-time system (about 30 frame/sec) based on FPGA, video camera, and monitor was built up to verify the model's behavior and to analyze the effect of parameter accuracy.…”

Section: Introductionmentioning

confidence: 99%

FPGA-Based Real Time, Multichannel Emulated-Digital Retina Model Implementation

Vörösházi

Nagy

Szolgay

2009

EURASIP J. Adv. Signal Process.

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The function of the low-level image processing that takes place in the biological retina is to compress only the relevant visual information to a manageable size. The behavior of the layers and different channels of the neuromorphic retina has been successfully modeled by cellular neural/nonlinear networks (CNNs). In this paper, we present an extended, application-specific emulated-digital CNN-universal machine (UM) architecture to compute the complex dynamic of this mammalian retina in video real time. The proposed emulated-digital implementation of multichannel retina model is compared to the previously developed models from three key aspects, which are processing speed, number of physical cells, and accuracy. Our primary aim was to build up a simple, real-time test environment with camera input and display output in order to mimic the behavior of retina model implementation on emulated digital CNN by using low-cost, moderate-sized field-programmable gate array (FPGA) architectures.

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Low-error, high-speed, and large-scale hardware implementation of retinal photoreceptor cells: Cone and rod cells

Ghanbarpour,

Ghanbarpour

2024

AEU - International Journal of Electronics and Communications

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An Emulated Digital Retina Model Implementation on FPGA

Cited by 8 publications

References 2 publications

Optimized cellular neural network universal machine emulation on FPGA

Optimized cellular neural network universal machine emulation on FPGA

FPGA-Based Real Time, Multichannel Emulated-Digital Retina Model Implementation

Low-error, high-speed, and large-scale hardware implementation of retinal photoreceptor cells: Cone and rod cells

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