关键词: 卷积, 深度学习, Winograd算法, 快速傅里叶变换

Abstract: Convolutional neural network is one of the most widely applied directions of deep learning algorithms. At present, the application of convolutional neural network is not only in the field of science and technology, but also in medical, military and other fields, and has played a huge role in related fields. Convolution is the most core part of convolutional neural network, and the computation amount of convolution accounts for more than 70% of the time of the whole network. Therefore, it is very important to study the acceleration of convolution operation. Firstly, the convolution algorithms in recent years are introduced, and their complexity is analyzed. The advantages and disadvantages of these algorithms are summarized. Finally, the possible breakthroughs in theoretical research and application are discussed and prospected.

Key words: convolution, deep learning, Winograd algorithm, FFT