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AI-NET CELTIC Innovation Award Winning project 2024 1 месяц назад


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AI-NET CELTIC Innovation Award Winning project 2024

AI-NET showed outstanding achievements in the area of 6G enabling technologies complementing 5G solutions with focus on edge centric compute and AI, shaping new secure services and application platforms. Excellent results for sustainable compute platform supporting AI on top of the communications system have been successfully demonstrated. AI-NET showed highly competitive solutions for example in the area of energy efficient edge data centres. Data centre solutions are close to the best hyper scaler solution that reached power aware effectiveness of 1-1.2 PUE. That clearly outlines the competitive edge that the project has met to meet global challenges for Europs future prosperity and competitiveness. Scientific excellence in the area of future 6G Technologies as well as reaching ambitious KPIs to secure future markets that can enable 6G Technologies have been shown. Overall 316 scientific publications in leading international journals, conferences supported by 184 PhD and Master Thesis have been accepted and successfully finished. High business outcome is expected due to the 72 IPR, OpenSource and Standards contributions. With overall 77 Proof of Concepts, and 70 keynotes and 43 hirings the project underlines its excellence even further. The project worked on timely solutions with regards to security, sustainability, performance and cost reduction of AI in future telecom networks. Energy Metering measuring energy consumption in real industry environments were only one part the project next to hybrid IT management solutions, incremental manufacturing. Optimized power distribution in synergy with the network management and the lightweight software layer solutions can be used as first entry point for the Telecom customer to access a Telco cloud. New AI algorithms that showed federated learning (FL) next to unlearning, analysing overfitting parametrization next to smart clustering solutions promised data saving in large scale once deployed on a computing platform.

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