Edge and Fog Computing Enabled AI for IoT - An Overview

Zhuo Zou, Yi Jin, Paavo Nevalainen, Yuxiang Huan, Jukka Heikkonen, Tomi Westerlund

In recent years, Artificial Intelligence (AI) has been widely deployed in a variety of business sectors and industries, yielding numbers of revolutionary applications and services that are primarily driven by high-performance computation and storage facilities in the cloud. On the other hand, embedding intelligence into edge devices is highly demanded by emerging applications such as autonomous systems, human-machine interactions, and the Internet of Things (IoT). In these applications, it is advantageous to process data near or at the source of data to improve energy \& spectrum efficiency and security, and decrease latency. Although the computation capability of edge devices has increased tremendously during the past decade, it is still challenging to perform sophisticated AI algorithms in these resource-constrained edge devices, which calls for not only low-power chips for energy efficient processing at the edge but also a system-level framework to distribute resources and tasks along the edge-cloud continuum. In this overview, we summarize dedicated edge hardware for machine learning from embedded applications to sub-mW “always-on” IoT nodes. Recent advances of circuits and systems incorporating joint design of architectures and algorithms will be reviewed. Fog computing paradigm that enables processing at the edge while still offering the possibility to interact with the cloud will be covered, with focus on opportunities and challenges of exploiting fog computing in AI as a bridge between the edge device and the cloud.