Massively parallel implementation of sparse message retrieval algorithms in Clustered Clique Networks

Philippe Tigréat 1, 2 Pierre-Henri Horrein 2, 1 Vincent Gripon 1, 2
1 Lab-STICC_TB_CACS_IAS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : Auto-associative memories are a family of algorithms designed for pattern completion. Many of them are based on neural networks, as is the case for Clustered Clique Networks which display competitive pattern retrieval abilities. A sparse variant of these networks was formerly introduced which enables further improved performances. Specific pattern retrieval algorithms have been proposed for this model, such as the Global-Winners-Take-All and the Global-Losers-Kicked-Out. We hereby present accelerated implementations of these strategies on graphical processing units (GPU). These schemes reach interesting factors of acceleration while preserving the retrieval performance.
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Communication dans un congrès
The 2016 International Conference on High Performance Computing & Simulation (HPCS2016), Jul 2016, Innsbrück, Austria. pp.935 - 939, 2016, High Performance Computing & Simulation (HPCS), 2016 International Conference on. <10.1109/HPCSim.2016.7568434>
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Soumis le : samedi 15 avril 2017 - 18:41:43
Dernière modification le : mercredi 19 avril 2017 - 01:10:10

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Philippe Tigréat, Pierre-Henri Horrein, Vincent Gripon. Massively parallel implementation of sparse message retrieval algorithms in Clustered Clique Networks. The 2016 International Conference on High Performance Computing & Simulation (HPCS2016), Jul 2016, Innsbrück, Austria. pp.935 - 939, 2016, High Performance Computing & Simulation (HPCS), 2016 International Conference on. <10.1109/HPCSim.2016.7568434>. <hal-01503968>

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