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Graphics processor unit hardware acceleration of Levenberg-Marquardt artificial neural network training
journal contribution
posted on 2013-09-05, 14:05 authored by David Scanlan, David MulvaneyThis 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.
History
School
- Mechanical, Electrical and Manufacturing Engineering
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.Publisher
© Research InventyVersion
- VoR (Version of Record)
Publication date
2013Notes
This article was published in the journal, Research Inventy: International Journal Of Engineering And Science.ISSN
2278-4721Publisher version
Language
- en