Visual AER-based processing with convolutions for a parallel supercomputer

This paper is based on the simulation of a convolution model for multimedia applications using the neuro-inspired Address-Event-Representation (AER) philosophy. AER is a communication mechanism between chips gathering thousands of spiking neurons. These spiking neurons are able to process the visual information in a frame-free style like the human brain do. All the spiking neurons are working in parallel and each of them implement an operation when an input stimulus is received. The result of this operation could be, or not, to produce an output event. There exist AER retinas and other sensors, AER processors (convolvers, WTA filters), learning chips and robot actuators. In this paper we present the implementation of an AER convolution processor for the supercomputer CRS (cluster research support) of the University of Cadiz (UCA). This research involves a test cases design in which the optimal parameters are set to run the AER convolution in parallel processors. These cases consist on running the convolution taking an image divided in different number of parts, applying to each part a Sobel filter for edge detection, and based on the AER-TOOL simulator. Runtimes are compared for all cases and the optimal configuration of the system is discussed. In general, CRS obtain better performances when the image is subdivided than for the whole image processing.