Edge artificial intelligence (AI) combines AI, the Internet of Things (IoT) and edge computing technologies to provide real-time collection, processing, analytics, and decision-making. 

The edge AI technologies and applications are constantly evolving and are driving future intelligent control and decision-making by moving AI capabilities closer to the physical world to enable the implementation of autonomous systems.

The development of edge AI is supported by advancements in edge processing devices with novel processing architectures, including CPUs, GPUs, TPUs, ASICs, FPGAs, system on chip (SoC), system on module (SoM), and neuromorphic accelerators.

The expansion of the micro-, deep-, and meta-edge continuum as part of the novel distributed AI-based computing architectures support the emerging intelligent autonomous systems and applications across various industrial sectors.

The European Conference on EDGE AI Technologies and Applications – EEAI brings together researchers, practitioners, and edge intelligence champions to convey and exemplify their perspectives on energy-efficient edge AI architectures, frameworks, and platforms.

The conference addresses the edge AI foundations, applications, algorithms, software, communication networks, and systems. It allows showcasing the latest advancements in these areas, providing an arena for debating/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 and feature several leading voices in research and industry supporting the integration of edge AI technologies for advancing future industrial applications.

The topics of interest include, but are not limited to, the followings:

Edge AI Technologies

• Edge AI tools and methods
• Edge AI auto ML
• Architectures, frameworks, and protocols for intelligent edge processing
• Machine vision: image classification, object detection, semantic segmentation
• Energy optimisation for edge devices
• Reconfigurable edge AI 
• Optimisation methods and techniques for neural networks
• Multimodal learning implications for edge AI training and inference
• Federated learning
• Edge AI accelerators architectures
• Neuromorphic Architectures
• RISC-V-based edge AI hardware acceleration
• Edge AI heterogeneous systems integration
• Smart connectivity at the edge
• Simulation and analysis techniques for edge intelligence
• Human to edge AI interaction and its implications for privacy 
• Edge AI explainability and interpretability
• Generative edge AI
• Integrated hardware and software edge platforms 
• AI hardware-software co-design
• Edge AI workflows and systems design
• Integrating transformers: data, hardware, and software engineering challenges
• Trustworthy edge AI 
• Edge AI for sustainability and sustainable edge AI
• Edge AI system holistic approach: converging hardware, software, AI technology stack and data

 Edge AI Applications

• Digital industry
• Energy
• Agri-food and beverage
• Intelligent mobility systems
• Digital society
• Healthcare

Ovidiu Vermesan, SINTEF, Norway

Mario Diaz Nava, STMicroelectronics, France

Björn Debaillie, imec, Belgium

Marcello Coppola, STMicroelectronics, France      

Carmelo Pino, STMicroelectronics, Italy

Patrick Pype, NXP Semiconductors, Belgium

Huascar Espinoza, Key Digital Technologies Joint Undertaking, Belgium

Paolo Azzoni, Inside Industry Association, Netherlands

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

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

Konstantinos Georgopoulos, Technical University of Crete, Greece

Iakovos Mavroidis, Technical University of Crete, Greece

Fabrice Auzanneau, CEA-LIST, France

Fabian Chersi, CEA-LIST, France

Michael Karner, Virtual Vehicle Research, Austria

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

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

Loreto Mateu, Fraunhofer IIS, Germany

Alexandre Valentian, CEA-LIST, France

Andrea Dunbar, CSEM, Switzerland

Jochen Koszescha, Infineon, Germany

George Dimitrakopoulos, Infineon, Germany

Holger Schmidt, Infineon, Germany

Kay Bierzynski, Infineon, Germany

Roy Bahr, SINTEF, Norway

Ihsen Alouani, Université Polytechnique Hauts-De-France, France

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