Edge Large AI Models: Collaborative Deployment and IoT Applications

Large artificial intelligence models (LAMs) emulate human-like problem-solving capabilities across diverse domains, modalities, and tasks. By leveraging the communication and computation resources of geographically distributed edge devices, edge LAMs enable real-time intelligent services at the network edge. Unlike conventional edge AI, which relies on small or moderate-sized models for direct feature-to-prediction mappings, edge LAMs leverage the intricate coordination of modular components to enable context-aware generative tasks and multi-modal inference. We shall propose a collaborative deployment framework for edge LAM by characterizing the LAM intelligent capabilities and limited edge network resources. Specifically, we propose a collaborative training framework over heterogeneous edge networks that adaptively decomposes LAMs according to computation resources, data modalities, and training objectives, reducing communication and computation overheads during the fine-tuning process. Furthermore, we introduce a microservice-based inference framework that virtualizes the functional modules of edge LAMs according to their architectural characteristics, thereby improving resource utilization and reducing inference latency. The developed edge LAM will provide actionable solutions to enable diversified Internet-of-Things (IoT) applications, facilitated by constructing mappings from diverse sensor data to token representations and fine-tuning based on domain knowledge.

Edge Large AI Models: Revolutionizing 6G Networks

Large artificial intelligence models (LAMs) possess human-like abilities to solve a wide range of real-world problems, exemplifying the potential of experts in various domains and modalities. By leveraging the communication and computation capabilities of geographically dispersed edge devices, edge LAM emerges as an enabling technology to empower the delivery of various real-time intelligent services in 6G. Unlike traditional edge artificial intelligence (AI) that primarily supports a single task using small models, edge LAM is featured by the need of the decomposition and distributed deployment of large models, and the ability to support highly generalized and diverse tasks. However, due to limited communication, computation, and storage resources over wireless networks, the vast number of trainable neurons and the substantial communication overhead pose a formidable hurdle to the practical deployment of edge LAMs. In this paper, we investigate the opportunities and challenges of edge LAMs from the perspectives of model decomposition and resource management. Specifically, we propose collaborative fine-tuning and full-parameter training frameworks, alongside a microservice-assisted inference architecture, to enhance the deployment of edge LAM over wireless networks. Additionally, we investigate the application of edge LAM in air-interface designs, focusing on channel prediction and beamforming. These innovative frameworks and applications offer valuable insights and solutions for advancing 6G technology.