Abstract: In order to solve the problem that it is difficult to deploy convolutional neural network model on embedded devices with limited computing and storage resources due to the increasing scale of parameters, a convolutional neural network model compression method is proposed to reduce the scale of parameters. It is found that the number of convolution layer parameters is related to the number of input and output feature maps and the size of convolution kernel, while the number of full connection layer parameters is large and difficult to be reduced significantly. The number of input and output feature maps is reduced by grouping convolution, and the convolution kernel size is reduced by convolution resolution. At the same time, the global average pooling layers are used to replace the fully connected layers to solve the problem of large number of parameters in the fully connected layers. The above methods are applied to LeNet5 and AlexNet for experiments, the experimental results show that the parameters of LeNet5 model can be reduced by 97% and the recognition accuracy can be reduced by less than 2 percentage points by using the combined compression method, the parameters of AlexNet model can be reduced by 95% and the recognition accuracy can be improved by 6.72 percentage points after compression. On the premise of ensuring the accuracy of convolutional neural network, the parameters of the model can be greatly reduced.

Key words: convolutional neural networks, parameter scale, grouping convolution, convolution resolution, global average pooling