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Communication Dans Un Congrès Année : 2016

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

Résumé

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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Dates et versions

hal-01503968 , version 1 (15-04-2017)

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Citer

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, ⟨10.1109/HPCSim.2016.7568434⟩. ⟨hal-01503968⟩
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