The thriving of artificial intelligence (AI) applications is driving the further evolution of wireless networks. It has been envisioned that 6G will be transformative and will revolutionize the evolution of wireless from "connected things" to "connected intelligence". However, state-of-the-art deep learning and big data analytics based AI systems require tremendous computation and communication resources, causing significant latency, energy consumption, network congestion, and privacy leakage in both of the training and inference processes. By embedding model training and inference capabilities into the network edge, edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence, thereby improving the efficiency, effectiveness, privacy, and security of 6G networks. In this paper, we shall provide our vision for scalable and trustworthy edge AI systems with integrated design of wireless communication strategies and decentralized machine learning models. New design principles of wireless networks, service-driven resource allocation optimization methods, as well as a holistic end-to-end system architecture to support edge AI will be described. Standardization, software and hardware platforms, and application scenarios are also discussed to facilitate the industrialization and commercialization of edge AI systems.
翻译:人工智能(AI)应用的蓬勃发展正在推动无线网络的进一步发展,预计6G将是变革性的,并将将无线从“连接的东西”革命化为“连接的情报”,然而,以人工智能为基础的最先进的深层次学习和大数据分析分析系统需要巨大的计算和通信资源,在培训和推理过程中造成大量的延缓、能源消耗、网络拥塞和隐私渗漏。通过将示范培训和推断能力嵌入网络边缘,强大的AI作为6G的破坏性技术,可以无缝地整合遥感、通信、计算和情报,从而提高6G网络的效率、有效性、隐私和安全性。在本文件中,我们将提供我们可扩展和可信赖的边缘AI系统的愿景,并综合设计无线通信战略和分散的机器学习模式。将介绍无线网络的新设计原则、服务驱动资源分配优化方法以及支持边缘AI的综合端对端系统架构。还将讨论标准化、软件和硬件平台以及应用设想方案,以促进顶端AI系统的工业化和商业化。