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

This paper addresses challenges in 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: the application layer generates multiple types of tasks; 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 deep reinforcement learning architecture for resource allocation; 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.