swMATH ID: 12814
Software Authors: Weninger, Felix
Description: Introducing CURRENNT: the Munich open-source CUDA recurrent neural network toolkit. In this article, we introduce CURRENNT, an open-source parallel implementation of deep recurrent neural networks (RNNs) supporting graphics processing units (GPUs) through NVIDIA’s Computed Unified Device Architecture (CUDA). CURRENNT supports uni- and bidirectional RNNs with Long Short-Term Memory (LSTM) memory cells which overcome the vanishing gradient problem. To our knowledge, CURRENNT is the first publicly available parallel implementation of deep LSTM-RNNs. Benchmarks are given on a noisy speech recognition task from the 2013 2nd CHiME Speech Separation and Recognition Challenge, where LSTM-RNNs have been shown to deliver best performance. In the result, double digit speedups in bidirectional LSTM training are achieved with respect to a reference single-threaded CPU implementation. CURRENNT is available under the GNU General Public License from url{http://sourceforge.net/p/currennt}.
Homepage: http://sourceforge.net/projects/currennt/
Keywords: parallel computing; deep neural networks; recurrent neural networks; long short-term memory
Related Software: PyBrain; RNNLIB; CUDA
Cited in: 1 Document

Cited by 1 Author

1 Weninger, Felix

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