An Emulated Digital CNN Implementation
Journal of VLSI Signal Processing Systems - Special issue on spatiotemporal signal processing with analog CNN visual microprocessors
CNNUC3: A Mixed-Signal 64 x 64 CNN Universal Chip
MICRONEURO '99 Proceedings of the 7th International Conference on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems
New emulated discrete model of CNN architecture for FPGA and DSP applications
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Using Reconfigurable Supercomputers and C-to-Hardware Synthesis for CNN Emulation
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
FPGA-based architecture for the real-time computation of 2-D convolution with large kernel size
Journal of Systems Architecture: the EUROMICRO Journal
Journal of Real-Time Image Processing
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In this paper we propose a retinal architecture that incorporates the neural circuits found in the different retinal regions. It is implemented in a reconfigurable system for observing in real time the contrast processing capabilities of each retinal region over the provided stimuli. The retina model is based on a discrete-time cellular neural network (DTCNN) that will be implemented on reconfigurable architecture (FPGA) with a time multiplexing approach. This architecture is able to incorporate 50 million neurons in its structure for processing video in real time. It has been observed that the contrast detection and the detail resolution are influenced by the convergence factor of neurons and by the lateral inhibition, specific characteristics of each neural circuit.