The European Conference on EDGE AI Technologies and Applications - EEAI 2025 explores the development, optimisation, and application of intelligent edge AI systems across hardware, software, and practical use cases.
Key Topics
- Generative edge AI
- Edge AI heterogeneous systems integration
- Energy-aware edge AI and power-adaptive inference
- Human-to-edge AI interaction and privacy
- Machine vision: image classification, object detection, semantic segmentation
- Transformers at the edge
- Compact and efficient generative architectures
- Federated learning
- Edge AI accelerators – GPUs, NPUs, TPUs, ASICs, FPGAs
- Safe, verifiable, explainable, interpretable AI for autonomous edge agents
- Optimisation methods for neural networks
- On-device modelling and predictive control
- Neuromorphic computing for ultra-low-power edge AI
- AI hardware-software co-design methods
- Compiler and firmware co-design for edge AI workloads
- Architectures, frameworks, and protocols for edge processing
- Benchmarking edge AI models
- Reconfigurable edge AI
- Advanced federated learning and swarm intelligence
- Agentic and embodied AI at the edge
- Edge AI explainability and interpretability
- RISC-V-based edge AI acceleration
- Multi-agent collaboration at the edge
- On-device continual learning
- Simulation and analysis techniques for edge intelligence
- Ethical implications of edge AI
- Ultra-low-bit quantization and binarization
- Smart connectivity at the edge
- Edge AI verification, validation, and testing
- Trustworthy edge AI systems
- Frameworks for generative and agentic edge AI
- Heterogeneous edge AI hardware integration
- Generative data augmentation for training
- Adversarial robustness and security for edge AI
- Sustainable edge AI technologies
- Edge AI auto ML
- Edge AI natural language processing and speech
- Immersive technologies with edge AI (XR/VR/AR/MR)
- Integrated hardware and software edge platforms
- Energy optimisation for edge devices
- Immersive experiences with real-time AI at the edge
- Edge AI workflows and systems design
- Edge-native reinforcement learning
- Neuromorphic hardware architectures
- Edge AI optimisation for 5G/6G
- Edge AI tools and methods
Who should Attend
- Industry practitioners
- Researchers
- Academics
