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Parallel implementation of artificial neural network training

S. Scanzio, S. Cumani, R. Gemello, F. Mana, P. Laface
Politec. di Torino, Turin, Italy
IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2010

@inproceedings{scanzio2010parallel,

   title={Parallel implementation of artificial neural network training},

   author={Scanzio, S. and Cumani, S. and Gemello, R. and Mana, F. and Laface, P.},

   booktitle={Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on},

   pages={4902–4905},

   organization={IEEE},

   year={2010}

}

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In this paper we describe the implementation of a complete ANN training procedure for speech recognition using the block mode back-propagation learning algorithm. We exploit the high performance SIMD architecture of GPU using CUDA and its C-like language interface. We also compare the speed-up obtained implementing the training procedure only taking advantage of the multi-thread capabilities of multi-core processors. Our approach has been tested by training acoustic models for large vocabulary speech recognition tasks, showing a 6 times reduction of the time required to train real-world large size networks with respect to an already optimized implementation using the Intel MKL libraries.
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