• DocumentCode
    2487729
  • Title

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

  • Author

    Nowotny, Thomas

  • Author_Institution
    Dept. of Inf., Univ. of Sussex, Brighton, UK
  • fYear
    2010
  • fDate
    18-23 July 2010
  • Firstpage
    1
  • Lastpage
    8
  • 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;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), The 2010 International Joint Conference on
  • Conference_Location
    Barcelona
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-6916-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.2010.5596358
  • Filename
    5596358
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2487729