Parallel implementation of neural networks training on graphic processing unit

Recently artificial neural network (ANN) especially the deep belief network (DBN) becomes more and more popular in the acoustic model training. In order to improve the speed of ANN, the Graphics Processing Unit (GPU) is used. This paper gives the training details of the Back-Propagation (BP) neural...

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Bibliographic Details
Published in2012 5th International Conference on Biomedical Engineering and Informatics pp. 1571 - 1574
Main Authors Yong Liu, Yeming Xiao, Li Wang, Jielin Pan, Yonghong Yan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2012
Subjects
acoustic model
BP neural network
GPU
speech recognition
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Summary:Recently artificial neural network (ANN) especially the deep belief network (DBN) becomes more and more popular in the acoustic model training. In order to improve the speed of ANN, the Graphics Processing Unit (GPU) is used. This paper gives the training details of the Back-Propagation (BP) neural network acoustic model for speech recognition on GPU, including the parallel reduction application and asynchronous implementation between CPU and GPU. It is 26 times faster than using the single thread Intel ® MKL(Math Kernel Library) implementation.
ISBN:9781467311830
1467311839
DOI:10.1109/BMEI.2012.6513078