Parallel implementation of artificial neural network training
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}
}
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.
June 14, 2011 by hgpu
