What's New: Today at the Intel Industrial Summit 2020, Intel announced new enhanced internet of things (IoT) capabilities. The 11th Gen Intel Core processors, Intel Atom x6000E series, and Intel Pentium and Celeron N and J series bring new artificial intelligence (AI), security, functional safety and real-time capabilities to edge customers. With a robust hardware and software portfolio, an unparalleled ecosystem and 15,000 customer deployments globally, Intel is providing robust solutions for the $65 billion edge silicon market opportunity by 2024. "By 2023, up to 70% of all enterprises will process data at the edge.1 11th Gen Intel Core processors, Intel Atom x6000E series, and Intel Pentium and Celeron N and J series processors represent our most significant step forward yet in enhancements for IoT, bringing features that address our customers' current needs, while setting the foundation for capabilities with advancements in AI and 5G." –John Healy, Intel vice president of the Internet of Things Group and general manager of Platform Management and Customer Engineering Why It's Important: Intel works closely with customers to build proofs of concept, optimize solutions and collect feedback along the way. Innovations delivered with 11th Gen Intel Core processors, Intel Atom x6000E series, and Intel Pentium and Celeron N and J series processors are a response to challenges felt across the IoT industry: edge complexity, total cost of ownership and a range of environmental conditions.
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.