Integration and Optimization of the Deep Learning Framework of Mobile AI Software in Android Systems
This research focuses on the integration and optimization of deep learning frameworks in AI software on Android systems,aiming to improve the operating efficiency and user experience of mobile AI applications.By exploring technologies such as quantization,pruning,knowledge distillation,and miniaturized models,the study analyzed in detail the specific implementation and effects of these technologies in optimizing deep learning models on mobile devices.The article adopts an optimization strategy for the Android system,effectively reducing the computational complexity and memory usage of the model,and improving the model's inference speed and energy efficiency.The research results show that these optimization technologies have significant application value on resource-constrained mobile devices,providing strong support for the further development of mobile smart applications.
mobile terminaldeep learning frameworkAndroid system
万方数据
维普
