Название: Advancing Edge Artificial Intelligence: System Contexts
Автор: Ovidiu Vermesan, Dave Marples
Издательство: River Publishers
Серия: River Publishers Series in Communications and Networking
Год: 2023
Страниц: 260
Язык: английский
Формат: pdf (true)
Размер: 14.6 MB

The intersection of Artificial Intelligence (AI), the Internet of Things (IoT) and Edge Computing has kindled the edge AI revolution that promises to redefine how we perceive and interact with the physical world through intelligent devices. Edge AI moves intelligence from the network centre to the devices at its edge, entrusting these endpoints to analyse data locally, make decisions, and provide real-time responses.

Recent advances in power-efficient high-performance embedded silicon make edge AI a viable proposition, albeit one requiring new distributed architectures and novel design concepts. Moving decision-making closer to the edge makes responses faster and systems more reliable, while the constant pressure to reduce network bandwidth demand and the need to contain spiralling data storage and operations costs help justify the engineering investment necessary to embrace this new paradigm. Further, moving to decentralised operation opens the door to a multitude of novel applications, covering immersive technologies and autonomous systems across fields as diverse as healthcare and industrial automation, personal assistance and prognostics, surgery, and process control. In the best tradition of systems engineering, the first stage of this transition process is understanding the application domain for edge AI deployment, the "system context".

This book presents some key topics and early thinking from the EdgeAI project, covering data backhaul technologies, lifecycle management, mechanisms for developing AIs at the edge and techniques for interacting with those AIs. It provides examples of application domains before concluding with a review of how edge AI systems can be understood by their users. It also examines and presents new results based on current investigations and activities in edge AI technologies, considering the future trends in autonomic systems, hyperautomation, AI engineering, generative AI, connectivity, and cybersecurity mesh.

Machine Learning (ML) models are being deployed in a wide range of domains owing to their capacity to deliver high performance across a range of challenging tasks including safety-critical and privacy-sensitive applications. Moreover, the computing requirements of increasingly complex ML models presents a significant challenge to the hardware industry. Against this backdrop, Federated Learning (FL) has emerged as a promising technique that enables privacy-preserving development of ML models on low-energy Edge devices. FL is a distributed approach that enables learning from data belonging to multiple participants, without compromising privacy since user data are never directly shared. Instead, FL relies on training a global model by aggregating knowledge from local models. Despite its reputation as a privacy-enhancing strategy, recent studies reveal its susceptibility to sophisticated attacks that can undermine integrity and, as well as disrupt their operations. Notably, the constraints posed by the limited hardware resources in edge devices compound these challenges. Gaining insight into these potential risks and exploring hardware-friendly solutions is vital for effectively implementing trustworthy and power-efficient FL systems in edge environments.

This book provides valuable insight to researchers working with edge AI technologies, machine and deep learning engineers, IoT designers, and intelligent systems developers looking to deploy intelligent solutions at the edge.

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