Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence

TL;DR

Edge Intelligence combines edge computing and AI to address data processing challenges at the network edge, enabling efficient AI model training and inference on edge devices, and fostering new interdisciplinary research opportunities.

Contribution

This paper introduces the concept of Edge Intelligence, categorizes its two main forms, and provides a comprehensive overview and research roadmap for this emerging field.

Findings

Defines Edge Intelligence and its two main forms.

Highlights the importance of integrating AI with edge computing.

Provides a research roadmap for future developments.

Abstract

Along with the rapid developments in communication technologies and the surge in the use of mobile devices, a brand-new computation paradigm, Edge Computing, is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications are thriving with the breakthroughs in deep learning and the many improvements in hardware architectures. Billions of data bytes, generated at the network edge, put massive demands on data processing and structural optimization. Thus, there exists a strong demand to integrate Edge Computing and AI, which gives birth to Edge Intelligence. In this paper, we divide Edge Intelligence into AI for edge (Intelligence-enabled Edge Computing) and AI on edge (Artificial Intelligence on Edge). The former focuses on providing more optimal solutions to key problems in Edge Computing with the help of popular and effective AI technologies while the latter studies how to carry out the entire process of building AI models, i.e., model training and inference, on the edge. This paper provides insights into this new inter-disciplinary field from a broader perspective. It discusses the core concepts and the research road-map, which should provide the necessary background for potential future research initiatives in Edge Intelligence.