Red neuronal convolucional rápida sin fotogramas para reconocimientos de dígitos

Abstract

In this paper a bio-inspired six-layer convolutional network (ConvNet) non-frame based for digit recognition is shown. The system has been trained with the backpropagation algorithm using 32x32 images from the MNIST database. The system can be implemented with already physically available spike-based electronic devices. 10000 images have been coded into events separated 50ns to test the non-frame based ConvNet system. The simulation results have been obtained using actual performance figures for existing AER (Address Event Representation) hardware components. We provide simulation results of the system showing recognition delays of a few microseconds from stimulus onset with a recognition rate of 93%. The complete system consists of 30 convolution modules.