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A systematic review of Green
WIREs Data Mining and Knowledge Discovery, 2023 AbstractWith the ever‐growing adoption of artificial intelligence (AI)‐based systems, the carbon footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to hold themselves accountable for the carbon emissions of the AI models they design and use.
Roberto Verdecchia +2 more
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A review of green AI research under carbon peaking and neutrality goals
Zhejiang dianli, 2023 The large-scale training of artificial intelligence （AI） has led to a significant surge in computational resource demands， energy consumption， and carbon emissions， which not only poses severe challenges to the realization of carbon peaking and ...
LU Yudong, CHEN Yi
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Breast, 2020 Integrated breast cancer care is complex, marked by multiple hand-offs between primary care and specialists over an extensive period of time. Communication is essential for treatment compliance, lowering error and complication risk, as well as handling ...
E.C. Moser, Gayatri Narayan
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AI as an Essential Element of a Green 6G [PDF]
IEEE Transactions on Green Communications and Networking, 2021 Since the very beginning of global R&D activities on fifth generation wireless communications (5G), IEEEPAR energy-efficiency (EE) has been considered explicitly as one of the essential key performance indicators for wireless networks, and attracted much attention from academia and industry.
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Asynchronous layerwise deep learning with MCMC on low-power devices [PDF]
ESAIM: Proceedings and SurveysWe present a new architecture to learn a light neural network using an asynchronous layerwise bayesian optimization process deployed on low-power devices. The procedure is based on a sequence of five modules.
Francois M. +6 more
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Beyond Red AI: A Green Agentic-XAI Framework, for Energy-Efficient and High-Fidelity Learning Analytics [PDF]
EPJ Web of ConferencesIn Educational Intelligent Systems, the quick appropriation of explainable AI seems to generate a real tension between pedagogical transparency, and computational sustainability. While trust-building methods like, SHAP (Shapley Additive Explanations) are
Himri Khadija +2 more
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Batching for Green AI - An Exploratory Study on Inference
2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 2023 The batch size is an essential parameter to tune during the development of new neural networks. Amongst other quality indicators, it has a large degree of influence on the model's accuracy, generalisability, training times and parallelisability. This fact is generally known and commonly studied.
Tim Yarally +4 more
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CRISP-ML(QS): Embedding Sustainability in AI-ML Development Lifecycle [PDF]
International Journal of Mathematical, Engineering and Management SciencesArtificial Intelligence (AI) in contemporary industry and society is omnipresent, with its applications permeating across enterprise functions such as manufacturing, supply chain and research & development, and enterprise goals such as efficiency ...
Sahil Garg, Garima Mittal
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Sustainability in Artificial Intelligence - Towards a Green AI Reference Model
, 2023 The interest in Green Artificial Intelligence (AI) is growing as AI research is increasingly focusing on and taking into account environmental sustainability.
Begic Fazlic, Lejla +4 more
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The Green AI Ontology: An Ontology for Modeling the Energy Consumption of AI Models [PDF]
, 2022 Modeling AI systems’ characteristics of energy consumption and their sustainability level as an extension of the FAIR data principles has been considered only rudimentarily.
Färber, M., Lamprecht, D.
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