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    GPU-Based Simulation of Cellular Neural Networks for Image Processing

    Dolan, Ryanne
    DeSouza, Guilherme
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    [PDF] GPUBasedSimulationCellularNeuralNetworks.pdf (3.263Mb)
    Date
    2009-06
    Format
    Article
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    Abstract
    The inherent massive parallelism of cellular neural networks makes them an ideal computational platform for kernelbased algorithms and image processing. General-purpose GPUs provide similar massive parallelism, but it can be difficult to design algorithms to make optimal use of the hardware. The presented research includes a GPU abstraction based on cellular neural networks. The abstraction offers a simplified view of massively parallel computation which remains reasonably efficient. An image processing library with visualization software has been developed to showcase the flexibility and power of cellular computation on GPUs. Benchmarks of the library indicate that commodity GPUs can be used to significantly accelerate CNN research and offer a viable alternative to CPU-based image processing algorithms.
    URI
    http://hdl.handle.net/10355/9206
    Part of
    Electrical and Computer Engineering publications (MU)
    Citation
    Proceedings of International Joint Conference on Neural Networks, Atlanta, Georgia, USA, June 14-19, 2009.
    Rights
    OpenAccess.
    This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
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    • Electrical Engineering and Computer Science publications (MU)

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