Imagine if your AI could run on a tiny battery instead of a giant, power-hungry server. This project makes AI 'leaner' by cleaning up the data it learns from and optimizing how it thinks. It's like teaching a student to pass a test using a few high-quality notes instead of reading an entire library of messy books.
AI systems currently consume too much energy and cost too much to run, while often lacking the transparency and legal compliance required for critical industries like healthcare and energy.
A system architecture for Green AI, a data curation pipeline using generative AI, and an AI orchestrator for energy optimization.
If you are a software provider dealing with high compute costs and strict privacy laws — this project developed a system for trustworthy AI and synthetic data generation that ensures legal compliance and lower energy use.
If you are a manufacturer dealing with limited power on field devices — this project developed federated learning tested on Raspberry Pi 5 that allows AI to work locally without needing a massive cloud connection.
If you are an operator dealing with unstable energy loads and data noise — this project developed an AI orchestrator and data curation pipeline that reduces computational waste and improves reliability in smart grids.
Based on available project data, specific pricing or licensing costs are not provided as the project is currently in the development and pilot phase.
Yes, the project is designed for scalability across edge and cloud environments and will be evaluated through four real-life demonstrators in sectors like robotics and healthcare.
Based on available project data, the IP details are not specified, but the project involves 31 partners including 18 industry members.
Yes, the project has conducted comparative GDPR analysis in Belgium, France, Spain, and Greece to ensure legal compliance and ethical AI design.
The project provides an AI orchestrator and monitoring tools like Prometheus, Grafana, and MLFlow to integrate energy tracking and performance optimization into existing workflows.
The consortium is heavily industry-driven, with 18 industrial partners (58% of the total 31 members), including 12 SMEs. This high ratio of commercial entities suggests the project is focused on market viability and practical application rather than just academic research, with a broad geographical reach across 12 countries.
Contact Institut Jozef Stefan in Slovenia
Contact us to connect with the RAIDO consortium for pilot opportunities.
