移动端AI软件的深度学习框架在安卓系统中的集成与优化  

Integration and Optimization of the Deep Learning Framework of Mobile AI Software in Android Systems

作　　者：冯康 FENG Kang(Miaozhendida(Beijing)Network Technology Co.,Ltd.,Beijing 100081)

机构地区：[1]秒针滴答(北京)网络技术有限公司,北京100081

出　　处：《软件》2024年第11期49-51,共3页Software

摘　　要：本研究聚焦于安卓系统AI软件中的深度学习框架集成与优化问题,旨在提高移动端AI应用的运行效率与用户体验。研究通过探讨量化、剪枝、知识蒸馏与小型化模型等技术,详细分析了这些技术在移动设备上优化深度学习模型的具体实现与效果。文章采用了针对安卓系统的优化策略,有效降低了模型的计算复杂度和内存占用,提升了模型的推理速度与能效。研究结果表明,这些优化技术在资源受限的移动设备上具有显著的应用价值,为移动端智能应用的进一步发展提供了有力支持。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.

关 键 词：移动端 深度学习框架 安卓系统 

分 类 号：TP311.52[自动化与计算机技术—计算机软件与理论]