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Descripción
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| In this paper, a study of the parallel adaptation of a Principal Component Analysis (PCA) algorithm and a S upport Vector Machine (S VM) classifier, both running on a Massively Parallel Processor Array (MPPA) platform ?a system that assembles 256 cores in 16 different clusters?, is presented. The aim of this research has been to exploit the potential offered by a manycore MPPA platform to implement in parallel both PCA and S VM, so as to minimize the required time to process a hyperspectral image. These two algorithms are a key part of a more general one intended to discriminate between cancer and normal tissues during neurosurgical procedures. Experimenting with medical brain images captured in two operating theaters, the processing time measured when executing both algorithms simultaneously has been compared to the one obtained when executing the same algorithms sequentially. As a result, an average speedup of 6.19 has been achieved. Consequently, these algorithms consume less than an 8% of the available time to process a hyperspectral image, considering this time as the one required for the hyperspectral sensor to capture a new image. | |
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Internacional
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Si |
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Nombre congreso
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XXXI Design of Circuits and Integrated Systems Conference |
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Tipo de participación
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960 |
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Lugar del congreso
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Granada, Spain |
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Revisores
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Si |
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ISBN o ISSN
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978-1-5090-4565-5 |
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DOI
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Fecha inicio congreso
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23/11/2016 |
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Fecha fin congreso
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25/11/2016 |
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Desde la página
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1 |
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Hasta la página
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6 |
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Título de las actas
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Proceedings of the XXXI Design of Circuits and Integrated Systems Conference |