Edge Intelligence: Concepts, Architectures, Applications, and Future Directions

Abstract

The nameedge intelligence, also known asEdge AI, is a recent term used in the past few years to refer to the confluence of machine learning, or broadly speaking artificial intelligence, with edge computing. In this article, we revise the concepts regarding edge intelligence, such as cloud, edge, and fog computing, the motivation to use edge intelligence, and compare current approaches and analyze application scenarios. To provide a complete review of this technology, previous frameworks and platforms for edge computing have been discussed in this work to provide the general view of the basis for Edge AI. Similarly, the emerging techniques to deploy deep learning models at the network edge, as well as specialized platforms and frameworks to do so, are review in this article. These devices, techniques, and frameworks are analyzed based on relevant criteria at the network edge, such as latency, energy consumption, and accuracy of the models, to determine the current state of the art as well as current limitations of the proposed technologies. Because of this, it is possible to understand the current possibilities to efficiently deploy state-of-the-art deep learning models at the network edge based on technologies such as artificial intelligence accelerators, tensor processing units, and techniques that include federated learning and gossip training. Finally, the challenges of Edge AI are discussed in the work, as well as the future directions that can be extracted from the evolution of the edge computing and Internet of Things approaches.