Parallel implementation of artificial neural network training
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 obtai...
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| creator | Scanzio, Stefano Cumani, Sandro Gemello, Roberto Mana, Franco Laface, P |
| description | 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. |
| doi_str_mv | 10.1109/ICASSP.2010.5495108 |
| format | Conference Proceeding |
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| language | eng |
| recordid | cdi_ieee_primary_5495108 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Acoustic testing Artificial Neural Network Artificial neural networks CUDA Fast Training Feedforward systems Focused Attention Back-Propagation GPU Hidden Markov models Libraries Matrix converters Multicore processing Speech recognition State estimation Vocabulary |
| title | Parallel implementation of artificial neural network training |
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