Dynamic inference techniques for deep neural networks

Abstract

With the development of deep neural network, dynamic inference techniques have attracted extensive attention. When the traditional static neural network is faced with complex samples, it will generate a lot of computational redundancy, resulting in a waste of computing resources. In order to solve this problem, on the basis of static network, experts use dynamic reasoning technology to improve the network, so that the network can calculate the samples. Therefore, the network structure has been fundamentally improved by performing operations such as layer skipping and leaving early. Compared with the traditional static networks, the improved model has greatly improved the speed and scale. At the same time, the accuracy of operation has also been greatly improved.