Hardware Implementation of a Desktop Supercomputer for High Performance Image Processing.

Abstract

Abstract : An efficient behavioral simulator for Cellular Neural Networks (CNN) is hereby reported. The simulator is capable of performing Single-Layer CNN simulations for any size of input image, thus a powerful tool for researchers investigating potential applications of CNN. This report presents an efficient algorithm exploiting the latency properties of Cellular Neural Networks along with numerical integration techniques; simulation results and comparisons are also presented. A novel approach to simulate the hardware of Cellular Neural Networks (CNN) is presented as well. The approach, time-multiplexing simulation, is prompted by the need to simulate hardware models and test hardware implementations of CNN. For practical size applications, due to hardware limitations. It is impossible to have a one-on-one mapping between the CNN hardware processors and all the pixels of the image.