Abstract
Spiking neural networks (SNN) are effective model inspired by neural networks in the brain. However, when networks increase in size towards the biological scale, it is time-consuming to simulate the networks using CPU programming. To solve this problem, Graphic Processing Units (GPU) provide a method to speed up the simulation. It is proposed and proved as a pertinent solution for implementation of large scale of neural networks. This paper presents a GPU implementation of SNN for edge detection. The approach is then compared with an equivalent implementation on an Intel Xeon CPU. The results show that the GPU approach provide about 37 times faster than the CPU implementation.
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Zhuo, Z., Wu, Q., Zhang, Z., Zhang, G., Huang, L. (2013). GPU Implementation of Spiking Neural Networks for Edge Detection. In: Huang, DS., Gupta, P., Wang, L., Gromiha, M. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2013. Communications in Computer and Information Science, vol 375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39678-6_25
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DOI: https://doi.org/10.1007/978-3-642-39678-6_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39677-9
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