Results 11 to 20 of about 180,976 (265)
Communications of the ACM, 2020 Creating efficiency in AI research will decrease its carbon footprint and increase its inclusivity as deep learning study should not require the deepest pockets.
Roy Schwartz 0001 +3 more
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Green AI - A multidisciplinary approach to sustainability. [PDF]
Environ Sci EcotechnolHuang J, Gopal S.
europepmc +4 more sources
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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Towards sustainable AI: a comprehensive framework for Green AI
Discover SustainabilityThe rapid advancement of artificial intelligence (AI) has brought significant benefits across various domains, yet it has also led to increased energy consumption and environmental impact.
Abdulaziz Tabbakh +5 more
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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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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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Systems, 2023 With the emergence of the digital economy, digital technologies—such as artificial intelligence (AI)—have provided new possibilities for the green development of enterprises.
Ying Ying, Xiaoyan Cui, Shanyue Jin
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