Title :
Parallel implementation of a spiking neuronal network model of unsupervised olfactory learning on NVidia® CUDA™
Author_Institution :
Dept. of Inf., Univ. of Sussex, Brighton, UK
Abstract :
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.
Keywords :
coprocessors; neural nets; parallel processing; unsupervised learning; GPU; NVidia CUDA; graphical processing unit; spiking neuronal network model; unsupervised olfactory learning;
Conference_Titel :
Neural Networks (IJCNN), The 2010 International Joint Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-6916-1
DOI :
10.1109/IJCNN.2010.5596358
