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

With the wide application of Deep Neural Networks (DNNs), their huge model size and computational resource requirements have become the main bottlenecks limiting their deployment on resource-constrained devices. For this reason, research on model compression and efficient inference algorithms has received extensive attention. Model compression significantly reduces the number of model parameters and computation through pruning, quantization, knowledge distillation, etc. Efficient inference algorithms achieve significant improvement in inference efficiency by optimizing the computation process and designing lightweight models. In this paper, we systematically sort out the main methods of deep neural network model compression and their research progress, discuss the key technologies of efficient inference algorithms, and propose a model compression and inference method that combines multiple optimization strategies. Experimental results show that the method in this paper significantly improves the inference efficiency under the premise of ensuring model accuracy. The combination of model compression and efficient inference is a key path to solve the bottleneck of deep learning applications, which has important theoretical significance and practical value for edge computing, mobile devices and other scenarios.