Edge AI represents the powerful fusion of AI, IoT, and edge computing, delivering real-time data collection, processing, analytics, and decision-making directly at the source. The conference covers the entire technology stack, from foundational hardware to cutting-edge applications:

Hardware and Architectures: Advancements in smart sensors, edge processing devices, and novel computing architectures—from CPUs, GPUs, NPUs, and TPUs to ASICs, FPGAs, System-on-Chip (SoC), System-on-Module (SoM), and neuromorphic accelerators.

Algorithms and Methods: Artificial and spiking neural networks, generative models, agentic frameworks, and the data types and methods that power them.

Systems and Platforms: The expansion of the micro-, deep-, and meta-edge continuum as part of novel distributed AI-based computing architectures.

Applications: Real-world implementations across manufacturing, agrifood, automotive, healthcare, and beyond, enabling intelligent autonomous systems across diverse industrial sectors.

The conference addresses the edge AI foundations, applications, algorithms, hardware, software, communication networks, and systems. It showcases the latest advancements in these areas and provides an arena for debating and identifying future directions and challenges.

The European Conference on Edge AI Technologies and Applications aims to serve as an innovative edge AI platform for exchanging ideas and concepts. It will feature several leading representatives in research and industry who support the integration of edge AI technologies to advance future industrial applications.

As disruptive technologies converge, a new reality emerges. Technology is moving past the era of passive data processing and into an age of intelligent action. These systems don’t just understand context; they generate creative solutions and execute complex tasks directly at the source. This is the decisive next step in digital evolution, the moment where our tools stop merely observing the world and begin acting intelligently within it. It’s a future where intelligent edge devices respond to commands and anticipate needs, where technology doesn’t just present data but creates art, and where autonomous systems-of-systems work alongside humans and machines to solve humanity’s most significant challenges.

In this context, the topics of interest include, but are not limited to, the following:

Edge AI Technologies

• Edge AI tools and methods
• AI hardware-software co-design methods and tools
• Edge AI workflows and systems design
• Edge AI auto ML
• Architectures, frameworks, and protocols for intelligent edge processing
• Machine vision: image classification, object detection, semantic segmentation
• Edge AI natural language processing, speech recognition and synthesis
• Energy optimisation for edge devices
• Reconfigurable edge AI
• Optimisation methods and techniques for neural networks
• Ultra-low-bit quantization and binarization
• Hardware-aware neural architecture search (NAS) and pruning
• Energy-aware edge AI and power-adaptive inference
• On-device continual learning
• On-device modelling and predictive control
• Multimodal learning implications for edge AI training and inference
• Federated learning
• Advanced federated learning and swarm intelligence
• Edge-native reinforcement learning
• Multi-agent collaboration at the edge
• Edge AI accelerators – GPUs, NPUs, TPUs, ASICs, FPGAs, etc.
• Neuromorphic computing for ultra-low-power edge AI
• Neuromorphic HW Architectures (digital, mixed signal, analog, spiking)
• RISC-V-based edge AI hardware acceleration
• Edge AI heterogeneous systems integration
• Heterogeneous edge AI: integrating CPUs, GPUs, NPUs, and TPUs in edge devices to optimize AI workloads
• Compiler and firmware co-design for edge AI workloads
• Smart connectivity at the edge
• Edge AI optimisation for 5G/6G
• Edge AI for immersive technologies (XR/VR/AR/MR, metaverse/omniverse/multiverse)
• Enhancing immersive experiences with real-time AI processing at the edge
• Simulation and analysis techniques for edge intelligence
• Human to edge AI interaction and its implications for privacy
• Integrated hardware and software edge platforms
• Agentic edge AI workflows
• Generative edge AI
• Compact and efficient generative architectures
• Frameworks for generative and agentic edge AI
• Generative data augmentation for edge model training
• Embodied and physical edge AI
• Transformers at the edge: data, hardware, and software engineering challenges
• Running language models and transformers on resource-constrained devices
• Generative edge AI and automated machine learning (AutoML)
• Sustainable edge AI technologies
• Trustworthy edge AI: (dependable edge AI systems – security, privacy, safety reliability, availability, maintainability, connectability, fairness, accountable, robustness, resilience)
• Adversarial robustness and security foe edge AI
• Edge AI verification, validation and testing
• Safe, verifiable, explainable, interpretable AI for autonomous edge agents
• Benchmarking edge AI models
• Edge AI explainability and interpretability
• Ethical implications of edge AI
• Edge AI system holistic approach: converging hardware, software, AI technology stack and data
• Industrial IoT, robotics, and critical infrastructure

Ovidiu Vermesan, SINTEF, Norway

Alain Pagani, German Research Center for Artificial Intelligence – DFKI, Germany

Marcello Coppola, STMicroelectronics, France      

Patrick Pype, NXP Semiconductors, Belgium

Joseph Micallef, University of Malta, Malta

Edward Gatt, University of Malta, Malta

Joseph Vella Wallbank, University of Malta, Malta

Luca Valcarenghi, Sant’Anna School of Advanced Studies, Italy

Konstantinos Georgopoulos, Technical University of Crete, Greece

Iakovos Mavroidis, Technical University of Crete, Greece

Huascar Espinoza, Key Digital Technologies Joint Undertaking, Belgium

Paolo Azzoni, Inside Industry Association, Netherlands

Inessa Seifert, EPoSS – European Platform of System Integrators, Germany

Björn Debaillie, imec, Belgium

Fabian Chersi, CEA-LIST, France

Hana Krichene, CEA-LIST, France

Pavel Smrz, COGNITECHNA s.r.o., Czech Republic

Andrea Dunbar, CSEM, Switzerland

Jochen Koszescha, Infineon, Germany

George Dimitrakopoulos, Infineon, Germany

Holger Schmidt, Infineon, Germany

Roy Bahr, SINTEF, Norway

Jean-Marc Bonnefous, TCS, UK

Nuria Pazos, HES-SO, Switzerland

Weronika Gasior, FBA, Spain

Oscar Deniz, UCLM, Spain

Alessandro Capotondi, UNIMORE, Italy

Viviane Potocnik, ETHZ, Switzerland

Haralampos Stratigopoulos, Sorbonne University, France

Jose Cano Reyes, Glasgow University, UK

Javier Prieto, University of Salamanca, Spain

Ander Garcia, VICOMTECH, Spain

Modris Greitans, Institute of Electronics and Computer Science, Latvia

Janis Arents, Institute of Electronics and Computer Science, Latvia

Andrea Acquaviva, University of Bologna, Italy

Francesco Barchi, University of Bologna, Italy

Paolo Meloni, University of Cagliari, Italy

Fetze Pijlman, Signify, Netherlands

Vincenzo Piuri, University of Milan, Italy

Fabio Scotti, University of Milan, Italy

Angelo Genovese, University of Milan, Italy

Marco Ottella, Xtremion Engineering Srls, Italy