Charting the Intelligence Frontiers - Edge AI Systems Nexus
1st Edition
Will be published approx. on 27. January 2026
Book
Hardback
410 pages
978-87-438-0884-8 (ISBN)
Description
We are witnessing a fundamental shift in the computing landscape, a paradigm shift where intelligence is rapidly migrating to the very edges of the network. This is the era of edge AI, which embeds decision-making, perception, and control directly into devices that shape our physical world.
This book is a selected collection of the presented work from the European Conference on EDGE AI Technologies and Applications (EEAI) held on 21-23 October 2024 in Cagliari, Sardinia, Italy.
The conference is part of a series of annual conferences that delve into the expanding continuum of micro-, deep-, and meta-edge architectures, which form the backbone of emerging intelligent autonomous systems across numerous sectors. From the complexities of advanced algorithms and the design of novel edge AI hardware accelerators to the architecture of next-generation communication networks, AI frameworks and software systems, the EEAI fosters a vibrant exchange of ideas that are following the future and actively defining it.
From industrial automation to environmental monitoring, from manufacturing to smart mobility, the expansion of micro-, deep-, and meta-edge AI processing is propelling a new class of autonomous systems. These systems thrive on distributed intelligence, leveraging advancements in edge AI hardware architectures, accelerators, performant algorithms, and advanced software frameworks.
The chapters in this volume celebrate this paradigm shift and offer a glimpse into the vibrant ecosystem shaping the future of edge AI technologies and applications. The numerous chapters describe novel solutions and rigorous investigations spanning verification and validation, federated learning, neuromorphic design, scalable architectures, sensor fusion, and human-machine collaboration, each chapter demonstrating the innovation wave fuelling Europe's edge AI landscape.
Together, these twenty chapters provide a rich, multi-layered, and deeply insightful perspective on the state of edge AI. Each chapter documents the achievements made and highlights the path forward, offering a compelling vision of a future where intelligence is seamlessly and securely integrated into the fabric of the real world.
This book is an essential guide for navigating, understanding, and contributing to the dynamic and rapidly evolving field of edge AI.
The real value of this book lies in its innovative, forward-looking perspective, offering a guided exploration of the latest scientific breakthroughs and practical advancements shaping intelligent systems at the edge.
For researchers, students, practitioners, and visionaries, this book provides a comprehensive roadmap for the next stage in the evolution of intelligent, connected systems at the edge.
This book is a selected collection of the presented work from the European Conference on EDGE AI Technologies and Applications (EEAI) held on 21-23 October 2024 in Cagliari, Sardinia, Italy.
The conference is part of a series of annual conferences that delve into the expanding continuum of micro-, deep-, and meta-edge architectures, which form the backbone of emerging intelligent autonomous systems across numerous sectors. From the complexities of advanced algorithms and the design of novel edge AI hardware accelerators to the architecture of next-generation communication networks, AI frameworks and software systems, the EEAI fosters a vibrant exchange of ideas that are following the future and actively defining it.
From industrial automation to environmental monitoring, from manufacturing to smart mobility, the expansion of micro-, deep-, and meta-edge AI processing is propelling a new class of autonomous systems. These systems thrive on distributed intelligence, leveraging advancements in edge AI hardware architectures, accelerators, performant algorithms, and advanced software frameworks.
The chapters in this volume celebrate this paradigm shift and offer a glimpse into the vibrant ecosystem shaping the future of edge AI technologies and applications. The numerous chapters describe novel solutions and rigorous investigations spanning verification and validation, federated learning, neuromorphic design, scalable architectures, sensor fusion, and human-machine collaboration, each chapter demonstrating the innovation wave fuelling Europe's edge AI landscape.
Together, these twenty chapters provide a rich, multi-layered, and deeply insightful perspective on the state of edge AI. Each chapter documents the achievements made and highlights the path forward, offering a compelling vision of a future where intelligence is seamlessly and securely integrated into the fabric of the real world.
This book is an essential guide for navigating, understanding, and contributing to the dynamic and rapidly evolving field of edge AI.
The real value of this book lies in its innovative, forward-looking perspective, offering a guided exploration of the latest scientific breakthroughs and practical advancements shaping intelligent systems at the edge.
For researchers, students, practitioners, and visionaries, this book provides a comprehensive roadmap for the next stage in the evolution of intelligent, connected systems at the edge.
More details
Series
Language
English
Place of publication
Gistrup
Denmark
Target group
College/higher education
Professional and scholarly
Academic, Postgraduate, and Professional Practice & Development
Illustrations
89 farbige Abbildungen, 4 Farbfotos bzw. farbige Rasterbilder, 11 s/w Zeichnungen, 85 farbige Zeichnungen, 32 s/w Tabellen, 11 s/w Abbildungen
32 Tables, black and white; 89 Illustrations, color; 11 Illustrations, black and white
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 29 mm
Weight
837 gr
ISBN-13
978-87-438-0884-8 (9788743808848)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Ovidiu Vermesan | Alain Pagani | Paolo Meloni
Charting the Intelligence Frontiers - Edge AI Systems Nexus
E-Book
01/2026
River Publishers
€0.00
Available for download
Ovidiu Vermesan | Alain Pagani | Paolo Meloni
Charting the Intelligence Frontiers - Edge AI Systems Nexus
E-Book
01/2026
River Publishers
€0.00
Available for download
Persons
Dr. Ovidiu Vermesan holds a PhD degree in microelectronics and a Master of International Business (MIB) degree. He is Chief Scientist at SINTEF Digital, Oslo, Norway. His research interests are intelligent systems integration, mixed-signal embedded electronics, analogue neu-ral networks, edge artificial intelligence and cognitive communication systems. Dr. Ver-mesan received SINTEF's 2003 award for research excellence for his work on implementing a biometric sensor system. He is currently working on projects addressing nanoelectronics, integrated sensor/actuator systems, communication, cyber-physical systems (CPSs) and the Industrial Internet of Things (IIoT), with applications in green mobility, energy, autonomous systems, and smart cities. He has authored or co-authored over 100 technical articles and conference papers. He is actively involved in the activities of the European partnership for Key Digital Technologies (KDT) Joint Undertaking (JU), now the Chips JU. He has coordinat-ed and managed various national, EU and other international projects related to smart sensor systems, integrated electronics, electromobility and intelligent autonomous systems such as E3Car, POLLUX, CASTOR, IoE, MIRANDELA, IoF2020, AUTOPILOT, AutoDrive, Archi-tectECA2030, AI4DI, AI4CSM. Dr. Vermesan actively participates in national, Horizon Eu-rope and other international initiatives by coordinating and managing various projects. He is a member of the Alliance for AI, IoT and Edge Continuum Innovation (AIOTI) board. He is currently the coordinator of the Edge AI Technologies for Optimised Performance Embedded Processing (EdgeAI) project.
Dr. Alain Pagani is Principal Researcher and deputy director of the Augmented Vision research department at the German Research Center for Artificial Intelligence (DFKI). His research interests include artificial intelligence, computer vision, image understanding, and extended reality. He is the coordinator of the Network of Excellence dAIEDGE regarding distributed AI at the edge, and the coordinator of the Horizon Europe project CORTEX2 regarding remote cooperation using extended reality. He is a lecturer at the University of Kaiserslautern-Landau, and he has published over 100 articles in conferences and journals. His research finds applications in eXtended Reality for tele cooperation (project CORTEX2), artificial intelligence and computer vision for human-robot cooperation (project FLUENTLY), artificial intelligence and augmented reality for analysis of extremely large data (project ExtremeXP). Since 2023, he has been a Research Fellow at DFKI, which is a recognition for outstanding scientific achievements and special achievements in technology transfer.
Dr. Paolo Meloni is currently associate professor at the Department of Electrical and Electronic Engineering (DIEE) at the University of Cagliari. He is author of several international papers and tutor of many bachelor and master students' thesis in Electronic Engineering. His research activity is mainly focused on the development of advanced digital systems and programming of multi-core on-chip architectures, with emphasis on the application-driven design and programming of SoCs and FPGAs.
Dr. Alain Pagani is Principal Researcher and deputy director of the Augmented Vision research department at the German Research Center for Artificial Intelligence (DFKI). His research interests include artificial intelligence, computer vision, image understanding, and extended reality. He is the coordinator of the Network of Excellence dAIEDGE regarding distributed AI at the edge, and the coordinator of the Horizon Europe project CORTEX2 regarding remote cooperation using extended reality. He is a lecturer at the University of Kaiserslautern-Landau, and he has published over 100 articles in conferences and journals. His research finds applications in eXtended Reality for tele cooperation (project CORTEX2), artificial intelligence and computer vision for human-robot cooperation (project FLUENTLY), artificial intelligence and augmented reality for analysis of extremely large data (project ExtremeXP). Since 2023, he has been a Research Fellow at DFKI, which is a recognition for outstanding scientific achievements and special achievements in technology transfer.
Dr. Paolo Meloni is currently associate professor at the Department of Electrical and Electronic Engineering (DIEE) at the University of Cagliari. He is author of several international papers and tutor of many bachelor and master students' thesis in Electronic Engineering. His research activity is mainly focused on the development of advanced digital systems and programming of multi-core on-chip architectures, with emphasis on the application-driven design and programming of SoCs and FPGAs.
Content
1. Edge AI Systems Verification and Validation 2. Pioneering the Hybridization of Federated Learning in Human Activity Recognition 3. Edge Intelligence Architecture for Distributed and Federated Learning Systems 4. Challenges and Performance of SLAM Algorithms on Resource-constrained Devices
5. Designing Accelerated Edge AI Systems with Model Based Methodology 6. EDGE AI Hardware Acceleration for Critical Systems: from FPGAs Automatic Hardware Generator to Versatile CGRAs Systems 7. Model Selection and Prompting Strategies in Resource Constrained Environments for LLM-based robotic system 8. Optimising ViT for Edge Deployment: Hybrid Token Reduction for Efficient Semantic Segmentation 9. Recent Trends in Edge AI: Efficient Design, Training and Deployment of Machine Learning Models 10. Scalable Sensor Fusion for Motion Localization in Large RF Sensing Networks 11. The Accountability Strikes Back: Decentralizing the Key Generation in CL-PKC with Traceable Ring Signatures 12. A TinyMLOps Framework for Real-world Applications 13. Transfer and Self-learning in Probabilistic Models 14. A novel hierarchical approach to perform on-device Energy Efficient Fault Classification 15. Discovering and Classifying Digital and Wooden Industries Products' Defects at the Edge by a Yolo/ResNet-based Approach and Beyond 16. Conscious Agents Interaction Framework for Industrial Automation 17. Neuromorphic IoT Architecture for Efficient Water Management: A Smart Village Case Study 18. Online AI Benchmarking on Remote Board Farms 19. Enhancing Sustainability in Water Resource Management: Optimizing Neural Network Architectures for Accurate Prediction of Water Stress in European Countries 20. Multi-step Object Re-identification on Edge Devices: A Pipeline for Vehicle Re-identification
5. Designing Accelerated Edge AI Systems with Model Based Methodology 6. EDGE AI Hardware Acceleration for Critical Systems: from FPGAs Automatic Hardware Generator to Versatile CGRAs Systems 7. Model Selection and Prompting Strategies in Resource Constrained Environments for LLM-based robotic system 8. Optimising ViT for Edge Deployment: Hybrid Token Reduction for Efficient Semantic Segmentation 9. Recent Trends in Edge AI: Efficient Design, Training and Deployment of Machine Learning Models 10. Scalable Sensor Fusion for Motion Localization in Large RF Sensing Networks 11. The Accountability Strikes Back: Decentralizing the Key Generation in CL-PKC with Traceable Ring Signatures 12. A TinyMLOps Framework for Real-world Applications 13. Transfer and Self-learning in Probabilistic Models 14. A novel hierarchical approach to perform on-device Energy Efficient Fault Classification 15. Discovering and Classifying Digital and Wooden Industries Products' Defects at the Edge by a Yolo/ResNet-based Approach and Beyond 16. Conscious Agents Interaction Framework for Industrial Automation 17. Neuromorphic IoT Architecture for Efficient Water Management: A Smart Village Case Study 18. Online AI Benchmarking on Remote Board Farms 19. Enhancing Sustainability in Water Resource Management: Optimizing Neural Network Architectures for Accurate Prediction of Water Stress in European Countries 20. Multi-step Object Re-identification on Edge Devices: A Pipeline for Vehicle Re-identification