Introducing CURRENNT: the Munich open-source CUDA recurrent neural network toolkit
Felix Johannes Weninger, Johannes Michael Bergmann, Björn Wolfgang Schuller · Spiral (Imperial College London) · 2015
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 http://sourceforge.net/p/currennt.