GREEN.DAT.AI aims to channel AI’s potential towards Europe's sustainability goals by developing novel energy-efficient large-scale data analytics services, ready for use in industrial AI-based systems while reducing environmental impact of data management processes.
The project seeks to reduce the carbon footprint of AI technologies by creating an AI-ready Data Space (DS) infrastructure that supports energy-efficient AI techniques, addressing critical business and societal challenges.
Key strategies include designing AI algorithms with lower energy consumption, optimising hardware and software to improve energy efficiency, and employing techniques such as hardware acceleration, model optimisation, and data compression. Scalability is crucial, ensuring that AI services can adapt to demand without significant increases in energy consumption, while maintaining or surpassing performance standards.
The project's vision includes developing smart AI-powered applications that transition computation from data centers to edge devices, thus reducing data flow and enhancing privacy and reaction times. Federated Learning (FL) mechanisms will enable data sharing with minimal data transfer, supported by a novel Toolbox of reusable energy-efficient AI services.
A benchmarking and evaluation framework will be developed to measure and compare energy efficiency of various AI services, addressing the need for accurate energy consumption models and energy-aware algorithms. These services will be assessed across 4 industries—Smart Energy, Smart Agriculture, Smart Mobility, and Smart Banking—through 6 distinct pilots leveraging data spaces.
DS will serve as a digital enabler for collaborative data-driven services, ensuring data sovereignty, transparency, and fairness, thus facilitating seamless data exchange, incorporating robust security mechanisms and supporting interoperability, data quality, and privacy. By combining DS with AI services, GREEN.DAT.AI will unlock new potential for AI applications, enhancing predictive capabilities, increasing efficiency, and automating tasks.
