边缘智能与协同计算: 前沿与进展

随着万物互联时代的到来,边缘设备规模急剧增加,海量数据在网络边缘产生,人工智能技术的飞速发展为分析和处理这些数据提供了强大的支撑.然而,传统云计算的集中处理模式难以满足用户对任务低时延和设备低功耗的需求,并带来数据隐私泄露的潜在隐患.与此同时,嵌入式高性能芯片的发展显著提升了边缘设备的计算能力,使其能够在边缘侧实时处理部分计算密集型任务.在此背景下,边缘计算和人工智能有机融合,孕育了一种新的计算范式:边缘智能.鉴于此,聚焦边缘智能与协同计算的前沿与进展,首先概述边缘计算、人工智能和边缘智能的相关背景、基本原理与发展趋势;然后从训练、推理和缓存3个方面回顾面向单个设备的边缘智能方法;接着从架构、技术和功能3个维度介绍多个设备合作实现边缘智能协同的相关工作;最后总结边缘智能在工业物联网、智慧城市和虚拟现实等领域的广泛应用.

Edge intelligence and collaborative computing: Frontiers and advances

With the advent of the Internet of Everything era, there has been a dramatic increase in the number of edge devices, leading to the generation of massive amounts of data at the network edge. The development of artificial intelligence (AI) technology provides powerful support for analyzing and processing these data. However, the traditional centralized processing model of cloud computing fails to meet users'' demands for low latency of tasks and low power consumption of devices. In addition, it poses potential threats to data privacy and security. At the same time, the development of embedded high-performance chips has greatly enhanced the computing capabilities of edge devices, enabling them to process computation-intensive tasks in real-time at the edge. In light of this, edge computing (EC) and AI are organically integrated, giving rise to a new computing paradigm known as edge intelligence (EI). This paper focuses on the frontiers and advances in EI and collaborative computing. Firstly, we introduce the relevant background, basic principles, and development trends of EC, AI, and EI. Secondly, we review EI methods for individual devices, covering edge training, edge inference, and edge caching. Thirdly, we present the collaborative EI works on multiple devices from the perspectives of architecture, technology, and functionality. Finally, we summarize the wide-ranging applications of EI in various fields, such as the industrial Internet of Things, smart cities, and virtual reality.