Research on pruning optimization techniques for neural networks

Abstract

Large deep neural networks have been deploying in more and more application scenarios due to their success in multiple application scenarios. However, deep neural networks are difficult to apply to devices with fewer resources, as the large models and huge demand for computing resources make this difficult. Pruning optimization, as a critical model compression method, has become an essential part of the deployment process of deep neural networks and has extreme significance. This article summarizes the methods of deep neural network pruning optimization technology, sorts out the current research status of pruning optimization technology, analyzes different fine-grained pruning optimization technologies based on the different fine-grained levels of pruning optimization technology, and comparing the characteristics of different fine-grained pruning optimization techniques. This article also introduces the development process and current development direction of different fine-grained pruning optimization technologies, compares the effectiveness differences between different fine-grained pruning optimization technologies and looks forward to the combination of pruning optimization technology and model quantification technology. The end of the paper summarizes pruning optimization techniques and provides prospects.