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。
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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