# Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

## TL;DR

This survey explores how integrating deep learning with edge computing enables real-time, efficient AI services at the network edge, addressing latency and efficiency challenges in current cloud-based systems.

## Contribution

It provides a comprehensive overview of application scenarios, implementation methods, enabling technologies, challenges, and future trends of edge intelligence and deep learning integration.

## Key findings

- Edge intelligence facilitates real-time AI at the network edge.
- Implementation of DL training and inference in edge frameworks is evolving.
- Challenges include resource constraints and data privacy concerns.

## 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.