The sixth-generation (6G) mobile networks are expected to feature the ubiquitous deployment of machine learning and AI algorithms at the network edge. With rapid advancements in edge AI, the time has come to realize intelligence downloading onto edge devices (e.g., smartphones and sensors). To materialize this version, we propose a novel technology in this article, called in-situ model downloading, that aims to achieve transparent and real-time replacement of on-device AI models by downloading from an AI library in the network. Its distinctive feature is the adaptation of downloading to time-varying situations (e.g., application, location, and time), devices' heterogeneous storage-and-computing capacities, and channel states. A key component of the presented framework is a set of techniques that dynamically compress a downloaded model at the depth-level, parameter-level, or bit-level to support adaptive model downloading. We further propose a virtualized 6G network architecture customized for deploying in-situ model downloading with the key feature of a three-tier (edge, local, and central) AI library. Furthermore, experiments are conducted to quantify 6G connectivity requirements and research opportunities pertaining to the proposed technology are discussed.

翻译：第六代（6G）移动网络预计将在网络边缘实现机器学习和AI算法的泛在部署。随着边缘AI技术的快速发展，将智能下载到边缘设备（如智能手机和传感器）的时代已经到来。为实现这一愿景，本文提出一种名为"原位模型下载"的新技术，旨在通过网络中的AI库实现设备端AI模型的透明实时替换。其显著特征在于能够根据动态变化的情境（如应用场景、地理位置和时间）、设备异构的存储计算能力以及信道状态自适应调整下载过程。该框架的核心组件是一组在深度层级、参数层级或比特层级动态压缩下载模型的技术，以支持自适应模型下载。我们进一步提出了一种定制化部署原位模型下载的虚拟化6G网络架构，其关键特征在于三级（边缘层、本地层和中央层）AI库。最后，通过实验量化了6G连接需求，并探讨了该技术相关的研究机遇。