arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2004.13351eess.SPcs.ITcs.LGmath.IT
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无线网络中通信高效的边缘AI推理

Communication-Efficient Edge AI Inference Over Wireless Networks

School of Information Science and Technology, ShanghaiTech University(上海科技大学信息科学与技术学院)

Kai Yang, Yong Zhou, Zhanpeng Yang, Yuanming Shi

摘要:

鉴于智能设备的快速增长,预计大量高风险人工智能(AI)应用,例如,无人驾驶飞机、自动驾驶汽车、触觉机器人,将在不久的将来部署在无线网络的边缘。因此,智能通信网络将被设计为利用先进的无线技术和边缘计算技术来支持各种终端设备上的AI应用,这些终端设备具有有限的通信、计算、硬件和能源资源。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的AI服务。这包括用于低延迟设备分布式模型推理的无线分布式计算框架以及用于节能边缘协作模型推理的无线协作传输策略。通过智能反射面建立智能无线电传播环境,进一步提高了边缘推理系统的通信效率。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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