PERFORMANCE COMPARISON OF A BIOLOGICALLY INSPIRED EDGE DETECTION ALGORITHM ON CPU, GPU AND FPGA

摘要

Implementation of Spiking neural networks (SNNs) are becoming an important computational platform for bio-inspired engineers and researchers. However, as networks increase in size towards the biological scale. Ever increasing simulation times are becoming a substantial problem. Efforts to simulate this problem have been many and varied. Modern Graphic Processing Units (GPUs) are increasingly being employed as a platform, whose parallel array of streaming multiprocessors (SMs) allow many thousands of lightweight threads to run. This paper presents a GPU implementation of an SNN application which performs edge detection. The approach is then compared with an equivalent implementations on an Intel Xeon CPU and an FPGA system. The GPU approach was found to provide a speed up of 1.37 times over the FPGA version and an increase of 23.49 times when compared with the CPU based software simulation.