Title :
Parallel implementation of neural networks training on graphic processing unit
Author :
Yong Liu ; Yeming Xiao ; Li Wang ; Jielin Pan ; Yonghong Yan
Author_Institution :
Key Lab. of Speech Acoust. & Content Understanding, Inst. of Acoust., Beijing, China
Abstract :
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.
Keywords :
acoustic signal processing; backpropagation; belief networks; graphics processing units; neural nets; parallel programming; speech recognition; ANN; CPU; DBN; GPU; acoustic model training; artificial neural network; asynchronous implementation; backpropagation neural network acoustic model; deep belief network; graphic processing unit; neural network training; parallel implementation; parallel reduction application; speech recognition; BP neural network; GPU; acoustic model; speech recognition;
Conference_Titel :
Biomedical Engineering and Informatics (BMEI), 2012 5th International Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4673-1183-0
DOI :
10.1109/BMEI.2012.6513078
