Editors:
Ovidiu Vermesan, SINTEF, Norway
Alain Pagani, German Research Center for Artificial Intelligence, Germany
Paolo Meloni, University of Cagliari, Italy
Authors:
Siemens EDA, Germany, Siemens EDA, USA
This book is the result of the rich exchanges of ideas and presentations at the
European Conference on EDGE AI Technologies and Applications (EEAI)
held on 21-23 October 2024 in Cagliari, Sardinia, Italy, offering a panoramic
snapshot and a technical deep dive into the contemporary landscape of
edge AI. With twenty selected chapters, it encapsulates the convergence of
fundamental concepts, technical advancements, and real-world deployments
that define the edge AI continuum.
Collectively, the book serves as a reference for the field, capturing
the current state-of-the-art and anticipating future trends in hyperautomation,
generative AI, connectivity, autonomy, and security mesh architectures.
Whether you are seeking in-depth technical knowledge, inspiration for novel
applications, or a strategic overview of the edge AI landscape, you will find
invaluable insights from thought researchers and practitioners at the forefront
of the field of edge AI.
A brief overview of each of the twenty chapters is provided below,
highlighting the research and applications of edge AI that underscore the
book’s commitment to both technological and societal impact.
Edge AI Systems Verification and Validation: This chapter explores the challenges
of verifying and validating complex edge AI systems, which integrate
hardware, software, and data. It proposes a structured framework that combines
model- and data-driven engineering to ensure these systems are reliable,
robust, and meet regulatory standards.
Edge AI technologies, edge AI technology stack, micro-edge, deep-edge, and meta-edge, neuromorphic computing, artificial intelligence (AI), edge AI accelerators, deep learning (DL), machine learning (ML), federated learning (FL), Internet of Things (IoT), system-on-chip (SoC), autonomous systems, edge AI trustworthiness, AI explainability (XAI), AI interpretability (IAI).