Advancing Edge Artificial Intelligence: System Contexts [Hardcover]

1. Edge AI LoRa Mesh Technologies 2. Edge AI Lifecycle Management 3. Federated Learning: Privacy, Security and Hardware Perspectives 4. Inside the AI Accelerators: From High Performance to Energy Efficiency 5. Designing Lightweight CNN for Images: Architectural Components and Techniques 6. Natural Language Conditioned Planning of Complex Robotics Tasks 7. An Overview of the Automated Optical Inspection Edge AI Inference System Solutions 8. Efficient AI-based Attack Detection Methods for Sensitive Edge Devices and Systems 9. Explainability and Interpretability Concepts for Edge AI Systems

The 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.

The intersection of 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.

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, personallcî