In-situ Model Downloading to Realize Versatile Edge AI in 6G Mobile Networks
This addresses the problem of deploying adaptive AI models on edge devices in 6G networks for applications like smartphones and sensors, representing an incremental advancement in edge AI infrastructure.
The paper tackles the challenge of enabling versatile edge AI in 6G networks by proposing in-situ model downloading, a technology that allows real-time replacement of on-device AI models from a network library, adapting to dynamic conditions and device constraints, with experiments quantifying 6G connectivity requirements.
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.