Excellence in Edge Artificial Intelligence

The convergence of AI and edge computing represents a new era of technological advances, where the immediacy of data processing and the sophistication of AI algorithms lead to new products and services. Industries are rapidly adopting edge AI applications to streamline costs, automate complex processes, enhance decision-making capabilities, and refine operational efficiency. These applications are not just theoretical constructs; they are real-world solutions that are reshaping industries. 

This workshop is dedicated to exploring the frontiers of the AI and edge computing synergy, focusing on the development of scalable and robust AI systems. The workshop is organized and supported by the dAIEdge European Network of Excellence on AI at the Edge. In order to make the workshop more interactive and lively, there will be a demo session. In the latter, participants will be able to show live demos, either on-site or with a video, of their latest research results. Participants in any session will be automatically invited to participate in and benefit from dAIEdge’s network of excellence. There will be a best paper award.

Call For Contributions

Topics

Topics of interest include (but are not limited to):

• Edge AI-based applications, use cases, products and services

• Edge AI tools and methodologies

• Computer vision: image classification, object detection, semantic segmentation, etc.

• Edge AI natural language processing, speech recognition and synthesis

• Energy optimization for edge AI devices

• Reconfigurable edge AI

• Optimization and approximation methods and techniques for edge neural networks

• Multimodal learning for edge AI

• Federated learning

• Edge AI accelerators and HW (GPUs, NPUs, TPUs, ASICs, FPGAs, RISC-V, etc.)

• Edge AI hardware-software co-design methods and tools

• Neuromorphic HW Architectures

• Smart connectivity at the edge

• Edge AI for immersive technologies (XR/VR/AR/MR, metaverse/omniverse/multiverse)

• Simulation and analysis techniques for edge AI

• Generative edge AI

• Transformers at the edge: data, hardware, and software engineering challenges

• Trustworthy edge AI (security, privacy, reliability, explainability, interpretability, etc.)

• Edge AI verification, validation and testing

• Benchmarking edge AI models

• Edge AI in space (Earth observation, satellites, etc.)

• Ethical implications of edge AI