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Training Recurrent Neural Network Using Multistream Extended Kalman Filter on Multicore Processor and Cuda Enabled Graphic Processor Unit

  • Michal Čerňanský
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5768)

Abstract

Recurrent neural networks are popular tools used for modeling time series. Common gradient-based algorithms are frequently used for training recurrent neural networks. On the other side approaches based on the Kalman filtration are considered to be the most appropriate general-purpose training algorithms with respect to the modeling accuracy. Their main drawbacks are high computational requirements and difficult implementation. In this work we first provide clear description of the training algorithm using simple pseudo-language. Problem with high computational requirements is addresses by performing calculation on Multicore Processor and CUDA-enabled graphic processor unit. We show that important execution time reduction can be achieved by performing computation on manycore graphic processor unit.

Keywords

Weight Connection Recurrent Neural Network Hide Unit Output Unit Previous Time Step 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Michal Čerňanský
    • 1
  1. 1.Faculty of Informatics and Information TechnologiesSTU BratislavaSlovakia

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