卷积神经网络在GPU上的实现

onvolutional neural network is a hot spot in the field of machine learning. It has been widely used in the field of image recognition. Although convolution neural network has good performance, but when face practical problems, sometimes the calculation scale of the convolutional neural network is too large and computation efficiency is low. In recent years, the general parallel computing technology of GPU has been developed rapidly, because the comput core number of GPU is much more than that of CPU. Therefore, when facing with highly parallel computing problems, the capability of GPU is far beyond the CPU. Recently NVIDIA launch its deep learning library CUDNN base on its general computing architecture of GPU. The library provides interfaces for common operations of deep learning, making it simple to develop deep learning applications on GPU. This paper use CUDNN library to realize the convolutional neural network on GPU, and test the acceleration effect relative to CPU.