Edge artificial intelligence (AI) has been a promising solution towards 6G to empower a series of advanced techniques such as digital twins, 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）是面向6G的极具前景的解决方案，能够赋能数字孪生、全息投影、语义通信和自动驾驶等一系列先进技术，以实现万物智能。边缘人工智能任务（包括边缘学习与边缘AI推理）的性能取决于三个高度耦合过程的质量：数据采集的感知、信息提取的计算以及信息传输的通信。然而，这三个模块需要竞争网络资源以提升各自的服务质量。因此，集成感知-通信-计算（ISCC）对于提高资源利用率以及实现边缘AI任务的定制化目标具有至关重要的意义。通过研究这三个模块之间的相互作用，本文在应用层和物理层层面，为联邦边缘学习任务与边缘AI推理任务提出了多种ISCC方案。