Overview of deep convolutional neural network pruning

In recent years, due to the rapid development of deep convolutional neural networks, deep learning model inference needs to consume a lot of computing resources. Most current edge devices cannot support deep learning applications with low latency, low power consumption, and high accuracy due to limited resources. Deep learning applications. Therefore, model compression and acceleration of deep networks are an effective solution, and network pruning that simplifies the model by removing redundant parameters in the inference stage is a hot research in this field in recent years. This paper divides the work into six aspects for a detailed analysis, combs the latest progress of deep neural network pruning technology from the perspective of different granular pruning and weight measurement standards, and finally points out the problems in the current research and analyzes Future research directions in the field of pruning.