gpu上脉冲神经网络多类仿真的可扩展性

2014 13th Mexican International Conference on Artificial Intelligence Pub Date : 2014-11-16 DOI:10.1109/MICAI.2014.21

Israel Tabarez Paz, N. Hernández-Gress, M. González-Mendoza, David González-Marrón

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引用次数: 0

摘要

本文主要研究了尖峰神经网络(SNN)在加速其学习时间方面的可扩展性。在gpu设备Ge Force 9400M和Ge Force650 GTX上对SNN算法进行仿真，比较学习时间。采用多类数据库进行分类，并对分类结果进行比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Scalability of Multiclass Simulation of Spiking Neural Networks on GPUs

This manuscript is focused on scalability of Spiking Neural Network (SNN) for acceleration of its learning time. Simulation of SNN algorithm was implemented on GPUs devices Ge Force 9400M and Ge Force650 GTX in order to compare the learning time. Multiclass database are used for classification and the results are compared.

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2014 13th Mexican International Conference on Artificial Intelligence

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