Project description
Making green machine learning algorithms
The greening of our communication networks is an emerging trend, but potentially threatened by energy intensive AI algorithms that are needed for complex optimisations. The EU-funded GreenML5G project will investigate how to reduce energy expenditure for deep reinforcement learning modules. The overall output of the project is to create green machine learning algorithms for radio resource management. The technology has widespread impact in other areas of autonomy and machine learning.
Objective
Artificial Intelligence (AI) is revolutionising a wide range of industries. Wireless networks with emerging high dimensional challenges are set to benefit from data-driven deep learning optimisation across layers. In particular, we expect that the deep supervised and deep reinforcement learning modules can resolve high-dimensionality inputs, achieve near optimal solutions, and efficiently scale via confederated learning. However, what is not well understood is the energy cost and carbon footprint of AI in future wireless networks. The danger is that intelligent networks are not green networks and that the recent progress made in green communication risk being undermined by the new breed of AI-based wireless communication. Here, in this project, we propose to develop green machine learning algorithms for radio resource management. This will lead to a future of intelligent and sustainable wireless networking.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationstelecommunications networksmobile network5G
- natural sciencescomputer and information sciencesartificial intelligencemachine learningreinforcement learning
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
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Keywords
Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
MK43 0AL Cranfield - Bedfordshire
United Kingdom