关键词: 卷积神经网络（CNN）, 轻量化, 卷积方式

Abstract: In order to better apply the convolutional neural network model to mobile and embedded devices, it is necessary to reduce the amount of model parameters and reduce computational complexity. Firstly, several popular solutions are briefly introduced. Next, six lightweight convolutional neural network models are elaborated, showing the computational complexity and parameter quantities of different network computing methods. The core building blocks of the model, the overall network structure and innovations are discussed. The classification accuracy of each network and conventional convolutional network on the ImageNet dataset is analyzed. Furthermore, comparing the techniques of lightening the weight of each network, the conclusion is drawn that the direct index is used instead of the indirect index when designing the model. At the same time, the importance of the residual structure to ensure the accuracy of the lightweight model is found. Finally, the development prospect of lightweight convolutional neural network is prospected.

Key words: Convolutional Neural Network（CNN）, lightweight, convolution method