Research on Model Compression and Efficient Inference Algorithm for Deep Neural Networks

With the rapid development of deep learning technology, Deep Neural Networks (DNNs) have demonstrated excellent performance in the fields of image recognition, natural language processing, etc. DNN models usually have large parameter scales and computational complexity, which leads to their application in resource-constrained scenarios (mobile devices and edge computing). The rapid development of the AI industry has led to significant limitations. The artificial intelligence industry is rapidly evolving, with deep neural networks making particularly significant advances. The price of neural network performance is complex network structure and huge computation. Simply pursuing network performance without considering the computational cost will greatly limit the application scenarios in which it can be deployed. In order to balance the model performance and the demand for computational resources, model compression techniques have emerged to effectively reduce the model parameter size and inference time through pruning, quantization, knowledge distillation, and other methods. The design of efficient inference algorithms further enhances the operational efficiency of compressed models in practical applications. This paper systematically discusses the main technical methods of deep neural network model compression and its core ideas, deeply analyzes the advantages and disadvantages of pruning, quantization and knowledge distillation methods and their applicable scenarios, and discusses the design and optimization strategies of efficient inference algorithms in combination with hardware acceleration technology. Finally, this paper demonstrates the practical value of model compression and inference algorithms through typical application scenarios, and looks forward to the future research direction in this field.