Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

TLDR

Ubiquitous sensors and smart devices generate massive data, and the shift from cloud to edge computing is driven by increasing computing power, yet latency and efficiency constraints in cloud architectures hinder widespread AI deployment. The paper aims to explore how edge computing can enable deep learning services by deploying them near data sources. It reviews application scenarios, practical implementation methods and enabling technologies for DL training and inference on customized edge frameworks, and discusses challenges and future trends. The survey consolidates communication, networking, and DL insights to clarify their interconnections and stimulate further discussion on Edge DL.

Abstract

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.