Hardware Implementation of a Desktop Supercomputer for High Performance Image Processing
Technical rept. 1 Feb-1 May 1994
TEXAS A AND M UNIV COLLEGE STATION DEPT OF ELECTRICAL ENGINEERING
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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.
- Computer Programming and Software