The advancement of generative artificial intelligence (GAI) has driven revolutionary applications like ChatGPT. The widespread of these applications relies on the mixture of experts (MoE), which contains multiple experts and selectively engages them for each task to lower operation costs while maintaining performance. Despite MoE, GAI faces challenges in resource consumption when deployed on user devices. This paper proposes mobile edge networks supported MoE-based GAI. We first review the MoE from traditional AI and GAI perspectives, including structure, principles, and applications. We then propose a framework that transfers subtasks to devices in mobile edge networks, aiding GAI model operation on user devices. We discuss challenges in this process and introduce a deep reinforcement learning based algorithm to select edge devices for subtask execution. Experimental results will show that our framework not only facilitates GAI's deployment on resource-limited devices but also generates higher-quality content compared to methods without edge network support.

翻译：生成式人工智能（GAI）的进步推动了ChatGPT等革命性应用的发展。这类应用的广泛部署依赖于混合专家模型（MoE）——该架构包含多个专家模块，可根据任务需求选择性激活，在保持性能的同时降低计算成本。然而，即便采用MoE技术，GAI在用户设备部署时仍面临资源消耗挑战。本文提出基于移动边缘网络的MoE-GAI系统。首先从传统AI与GAI双重视角回顾MoE的结构、原理及应用，继而提出将子任务迁移至移动边缘网络设备的新型框架，以辅助用户设备运行GAI模型。我们探讨了该过程中的关键挑战，并设计基于深度强化学习的算法用于选择执行子任务的边缘设备。实验结果表明，该框架不仅能促进GAI在资源受限设备上的部署，相较于无边缘网络支持的方法，还能生成更高质量的内容。