Large AI Models Empowered Edge Intelligence for Next-Gen Consumer Electronics

This article addresses challenges in the Internet of Consumer Electronics (ICE), such as random task arrivals, limited resources, and system stability, by proposing a collaborative computing framework that integrates edge intelligence with Lyapunov-based deep reinforcement learning (DRL). The framework adopts a three-tier architecture. 1) The application layer generates multiple types of tasks; 2) the intelligent decision-making layer incorporates large artificial intelligence (AI) models to extract global features and employs Lyapunov optimization to transform long-term stochastic problems into deterministic optimization while utilizing an actor-critic DRL architecture for resource allocation; and 3) the resource layer integrates distributed edge nodes to form a unified resource pool. Experiments demonstrate that the framework achieves efficient, stable, and scalable intelligent services on the edge.