A retinomorphic architecture based on discrete-time cellular neural networks using reconfigurable computing

This paper describes a novel architecture for the hardware implementation of non-linear multi-layer cellular neural networks (CNNs). This makes it feasible to design CNNs with millions of neurons accommodated in low price FPGA devices, being able to process standard video in real time. This architecture has been used to build a CNN-based model of the synapsis I of the fovea region, with the aim of implementing the basic spatial processing of the retina in reconfigurable hardware. The model is based on the receptive fields of the bipolar cells and mimics the retinal architecture achieving its processing capabilities.