Edge artificial intelligence (AI) has been a promising solution towards 6G to empower a series of advanced techniques such as digital twin, holographic projection, semantic communications, and auto-driving, for achieving intelligence of everything. The performance of edge AI tasks, including edge learning and edge AI inference, depends on the quality of three highly coupled processes, i.e., sensing for data acquisition, computation for information extraction, and communication for information transmission. However, these three modules need to compete for network resources for enhancing their own quality-of-services. To this end, integrated sensing-communication-computation (ISCC) is of paramount significance for improving resource utilization as well as achieving the customized goals of edge AI tasks. By investigating the interplay among the three modules, this article presents various kinds of ISCC schemes for federated edge learning tasks and edge AI inference tasks in both application and physical layers.

翻译：边缘人工智能（Edge AI）作为6G网络中有望赋能数字孪生、全息投影、语义通信及自动驾驶等先进技术以实现万物智能化的解决方案，其性能（包括边缘学习与边缘AI推理）高度依赖于三个紧密耦合过程的质量，即数据采集的感知过程、信息提取的计算过程与信息传输的通信过程。然而，这三个模块为提升各自服务质量需竞争网络资源。为此，通感算一体化（ISCC）对于提高资源利用率及实现边缘AI任务的定制化目标具有至关重要的意义。通过探究三个模块间的交互机理，本文在应用层与物理层提出了面向联邦边缘学习任务与边缘AI推理任务的多类通感算一体化方案。