Artificial Intelligence (AI), Cloud, 5G, and IoT are continuously advancing innovation that extends across business development all the way down to the consumer level. Critical innovations are emerging from the escalation of new technologies, including hybrid workforces, remote healthcare delivery, hyper-personalization, and zero-touch. These use cases are generating myriad benefits for both organizations and consumers, and inspiring new levels of efficiency, productivity, and engagement. We're currently witnessing a dynamic surge in technological advancement that has spawned the era of ubiquitous digital transformation, but these new technologies still need room to grow. Ronald van Loon is working in partnership with NVIDIA, and recently had the opportunity to discuss the technology trends and drivers shaping the post-pandemic future, and assess the role the Arm acquisition by NVIDIA is positioned to play in this development.
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.
Not that I'm a big fan of such titles, but when I look at what 5G will bring, it's clear most businesses will feel the impact. Most technologies have a slow adoption curve. This change of speed is going to catch most companies unaware. The technological improvements of better connectivity are apparent, but their consequences aren't. There are two big groups of problems that 5G's lower latency and high bandwidth will impact. On one side, we have those problems that we can solve with low computation and real-time responses. Think of any remote controller. There is minor computation needs on the controller side but needs fast reactions on the remoter actuator.