Optimized cellular neural network universal machine emulation on FPGA

摘要

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 times 30 pixel image in less than 30 mus. Moreover, this platform has been used to realize the visual system of an autonomous mobile robot.