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.

通过应用 in-situ 模型下载技术，可以在 6G 移动网络中实现发射设备（如智能手机和传感器）的智能化。该技术可提供透明和实时的模型替换，可以根据时间变化的情况（如应用，位置和时间），设备异构存储和计算能力以及信道状态进行自适应模型下载。一项关键技术是在深度级别，参数级别或比特级别上动态压缩下载模型，以支持自适应模型下载。为该框架量身定制了三层（边缘、本地和中心）AI 库的虚拟 6G 网络架构。此外，还进行了实验来量化 6G 连接要求，并讨论了有关所提出技术的研究机会。