论文标题
无线网络上的沟通效率边缘AI推断
Communication-Efficient Edge AI Inference Over Wireless Networks
论文作者
论文摘要
鉴于智能设备的快速增长，预计会在不久的将来将部署大量的高风险人工智能（AI）应用程序，例如无人机，自动驾驶汽车，触觉机器人，将部署在无线网络的边缘。因此，智能通信网络将旨在利用先进的无线技术和边缘计算技术来支持各种端设备的AI-ables应用程序，具有有限的通信，计算，硬件和能源。在本文中，我们将介绍在网络边缘有效部署模型推理的原则，以提供低延迟和节能AI服务。这包括用于低延迟设备分布模型推理的无线分布式计算框架以及用于节能边缘合作模型推断的无线合作传输策略。通过智能反射表面构建智能无线电传播环境，可以进一步提高边缘推理系统的沟通效率。
Given the fast growth of intelligent devices, it is expected that a large number of high-stake artificial intelligence (AI) applications, e.g., drones, autonomous cars, tactile robots, will be deployed at the edge of wireless networks in the near future. As such, the intelligent communication networks will be designed to leverage advanced wireless techniques and edge computing technologies to support AI-enabled applications at various end devices with limited communication, computation, hardware and energy resources. In this article, we shall present the principles of efficient deployment of model inference at network edge to provide low-latency and energy-efficient AI services. This includes the wireless distributed computing framework for low-latency device distributed model inference as well as the wireless cooperative transmission strategy for energy-efficient edge cooperative model inference. The communication efficiency of edge inference systems is further improved by building up a smart radio propagation environment via intelligent reflecting surface.
