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    Exploiting data parallelism in artificial neural networks with Haskell

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    HEARTSFIELD-MASTERS-REPORT.pdf (234.5Kb)
    Date
    2009-08
    Author
    Heartsfield, Gregory Lynn
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    Abstract
    Functional parallel programming techniques for feed-forward artificial neural networks trained using backpropagation learning are analyzed. In particular, the Data Parallel Haskell extension to the Glasgow Haskell Compiler is considered as a tool for achieving data parallelism. We find much potential and elegance in this method, and determine that a sufficiently large workload is critical in achieving real gains. Several additional features are recommended to increase usability and improve results on small datasets.
    Department
    Electrical and Computer Engineering
    Description
    text
    Subject
    Functional programming
    parallel programming
    neural networks
    URI
    http://hdl.handle.net/2152/ETD-UT-2009-08-280
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