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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/13092

Title: Graphics processor unit hardware acceleration of Levenberg-Marquardt artificial neural network training
Authors: Scanlan, David
Mulvaney, David J.
Keywords: Artificial neural networks
Graphics processor unit
Levenberg-Marquardt networks
Issue Date: 2013
Publisher: © Research Inventy
Citation: SCANLAN, D. and MULVANEY, D., 2013. Graphics processor unit hardware acceleration of Levenberg-Marquardt artificial neural network training. Research Inventy: International Journal of Engineering and Science, 2 (7), 7pp.
Abstract: This paper makes two principal contributions. The first is that there appears to be no previous a description in the research literature of an artificial neural network implementation on a graphics processor unit (GPU) that uses the Levenberg-Marquardt (LM) training method. The second is an initial attempt at determining when it is computationally beneficial to exploit a GPU’s parallel nature in preference to the traditional implementation on a central processing unit (CPU). The paper describes the approach taken to successfully implement the LM method, discusses the advantages of this approach for GPU implementation and presents results that compare GPU and CPU performance on two test data sets.
Description: This article was published in the journal, Research Inventy: International Journal Of Engineering And Science.
Version: Published
URI: https://dspace.lboro.ac.uk/2134/13092
Publisher Link: http://www.researchinventy.com/papers/v2i7/A0270107.pdf
ISSN: 2278-4721
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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