Introduction to the Special Issue on edge intelligence: Neurocomputing meets edge computing

Abstract

Recent years have witnessed the proliferation of mobile computing and Internet-of-Things (IoT), where billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Driving by this trend and the development of wireless communication, edge computing, an emerging computing paradigm, has received tremendous amount of interest. By pushing data storage, computing, and control closer to the network edge, edge computing has been widely recognized as a promising solution to meet the requirements of low latency, high scalability, prompt response, and energy efficiency. In the meanwhile, with the development of neural networks, Artificial Intelligence (AI) has been applied to a variety of disciplines and proved highly successful in a vast class of intelligent applications cross many domains.Edge intelligence, aiming to facilitate the deployment of neural networks on edge computing, has received significant attention. However, there are many challenges existing for a novel design of edge computing architecture to AI applications, and their co-optimization. For instance, conventional neural networks techniques usually entail powerful computing facilities (e.g., cloud computing platforms), while the entities at the edge may have only limited resources for computations and communications. This suggests that AI algorithms should be revisited for edge computing to AI models into the edge device for efficient processing. On the other hand, the adapted deployments of neural networks at the edge empower the efficient learning systems that can provide the “smartification” across different layers, e.g., from network communications to applications, and also involve collaborations across edge to cloud. Finally, designing algorithms for small-scale edge devices in a learning ambience is all the challenging as there are several conflicting issues to account for. These include, memory management, power management, and compute capability of a node, etc.