GPU Implementation of Spiking Neural Networks for Color Image Segmentation

摘要：

Spiking neural networks (SNN) are powerful computational model inspired by the human neural system for engineers and neuroscientists to simulate intelligent computation of the brain. Inspired by the visual system, various spiking neural network models have been used to process visual images. However, it is time-consuming to simulate a large scale of spiking neurons in the networks using CPU programming. Spiking neural networks inherit intrinsically parallel mechanism from biological system. A massively parallel implementation technology is required to simulate them. To address this issue, modern Graphic Processing Units (GPUs), which have parallel array of streaming multiprocessors, allow many thousands of lightweight threads to be run, is proposed and proved as a pertinent solution. This paper presents an approach for implementation of an SNN model which performs color image segmentation on GPU. This approach is then compared with an equivalent implementation on an Intel Xeon CPU. The results show that the GPU approach was found to provide a 31 times faster than the CPU implementation.

关键词： graphic processing units spiking neural network computer unified device architecture colore image segmentation

作者: Ermai Xie Martin McGinnity QingXiang Wu Jianyong Cai Rontai Cai

作者单位: Intelligent Systems Research Center University of Ulster at Magee Londonderry, BT48 7JL, Northern Ir School of Physics and OptoElectronics Technology Fujian Normal University Fuzhou, 350007, China

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 1260-1264

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）