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Parallel implementation of a spiking neuronal network model of unsupervised olfactory learning on NVidia® CUDA™

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1 Author(s)
Nowotny, T. ; Dept. of Inf., Univ. of Sussex, Brighton, UK

In this work I present the parallel implementation of a spiking neuronal network model with biologically realistic morphology, elements, and function on a graphical processing unit (GPU) using the NVidia® CUDA™ framework. The comparison to a well-designed C/C++ implementation of the same model reveals a 24× speedup when using an NVidia® Tesla™ C870 device for the CUDA™ implementation and a 3 GHz AMD® Phenom™ II X4 940 processor for the classical implementation. With this speedup, the CUDA™ program can run the model comprising 2670 neurons and on the order of 200,000 synapses in faster than real time.

Published in:

Neural Networks (IJCNN), The 2010 International Joint Conference on

Date of Conference:

18-23 July 2010