Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network. As an important enabler broadly changing people’s lives, from face recognition to ambitious smart factories and cities, developments of artificial intelligence (especially deep learning, DL) based applications and services are thriving. However, due to efficiency and latency issues, the current cloud computing service architecture hinders the vision of “providing artificial intelligence for every person and every organization at everywhere”. Thus, unleashing DL services using resources at the network edge near the data sources has emerged as a desirable solution. Therefore, edge intelligence, aiming to facilitate the deployment of DL services by edge computing, has received significant attention. In addition, DL, as the representative technique of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge maintenance and management. With regard to mutually beneficial edge intelligence and intelligent edge, this paper introduces and discusses: 1) the application scenarios of both; 2) the practical implementation methods and enabling technologies, namely DL training and inference in the customized edge computing framework; 3) challenges and future trends of more pervasive and fine-grained intelligence. We believe that by consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge, i.e., Edge DL.

中文翻译：

---

边缘计算与深度学习的融合：综合调查

来自工厂和社区的无处不在的传感器和智能设备正在产生大量数据，不断增长的计算能力正在推动计算和服务的核心从云到网络边缘。作为广泛改变人们生活的重要推动者，从人脸识别到雄心勃勃的智能工厂和城市，基于人工智能（尤其是深度学习，DL）的应用和服务的发展正在蓬勃发展。但是，由于效率和延迟问题，当前的云计算服务架构阻碍了“为每个人和每个组织随时随地提供人工智能”的愿景。因此，使用靠近数据源的网络边缘的资源来释放 DL 服务已成为一种理想的解决方案。因此，边缘智能，旨在通过边缘计算促进DL服务的部署，受到了极大的关注。此外，DL作为人工智能的代表技术，可以集成到边缘计算框架中，构建智能边缘，实现动态、自适应的边缘维护和管理。针对互利的边缘智能和智能边缘，本文介绍和讨论：1）两者的应用场景；2）实际的实现方法和使能技术，即定制化边缘计算框架中的DL训练和推理；3）更普遍和更细粒度的智能的挑战和未来趋势。我们相信，通过整合分散在通信、网络和深度学习领域的信息，