With the advent of emerging IoT applications such as autonomous driving, digital-twin and metaverse etc. featuring massive data sensing, analyzing and inference as well critical latency in beyond 5G (B5G) networks, edge artificial intelligence (AI) has been proposed to provide high-performance computation of a conventional cloud down to the network edge. Recently, convergence of wireless sensing, computation and communication (SC${}^2$) for specific edge AI tasks, has aroused paradigm shift by enabling (partial) sharing of the radio-frequency (RF) transceivers and information processing pipelines among these three fundamental functionalities of IoT. However, most existing design frameworks separate these designs incurring unnecessary signaling overhead and waste of energy, and it is therefore of paramount importance to advance fully integrated sensing, computation and communication (ISCC) to achieve ultra-reliable and low-latency edge intelligence acquisition. In this article, we provide an overview of principles of enabling ISCC technologies followed by two concrete use cases of edge AI tasks demonstrating the advantage of task-oriented ISCC, and pointed out some practical challenges in edge AI design with advanced ISCC solutions.

翻译：随着自动驾驶、数字孪生、元宇宙等新兴物联网应用的出现，这些应用在超5G（B5G）网络中涉及海量数据感知、分析与推理以及关键延迟需求，边缘人工智能（Edge AI）被提出以将传统云计算的高性能计算能力下沉至网络边缘。近年来，针对特定边缘AI任务的无线感知、计算与通信（SC²）融合，通过允许物联网三大基础功能（部分）共享射频收发器与信息处理流水线，引发了范式转变。然而，现有大多数设计框架将三者分离设计，导致不必要的信令开销与能源浪费。因此，推进完全集成的感知、计算与通信（ISCC）以实现超可靠低延迟边缘智能获取至关重要。本文首先概述了ISCC支撑技术的原理，随后通过两个具体边缘AI任务用例展示了面向任务ISCC的优势，并指出了采用先进ISCC方案进行边缘AI设计时面临的实际挑战。