Parallel implementation of neural networks training on graphic processing unit
Yong Liu, Yeming Xiao, Li Wang, Jielin Pan, Yonghong Yan · 2012
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.