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Deep learning audio development costs 3-256x more energy than training, study finds

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv highlights the significant environmental costs associated with the development phase of deep learning audio projects. Researchers from the LORIA laboratory, using data from the Grid5000 computing platform, found that the energy consumed during architecture prototyping and experimentation can be 3 to 256 times greater than the energy required to train the final best-performing model. The findings advocate for more comprehensive reporting of energy consumption throughout the entire lifecycle of deep learning projects. AI

IMPACT Highlights the substantial, often overlooked, energy costs of AI development, urging for more sustainable practices.

RANK_REASON Research paper published on arXiv detailing methodology and findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep learning audio development costs 3-256x more energy than training, study finds

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Research paper published on arXiv detailing methodology and findings. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Constance Douwes, Paul Magron, Romain Serizel ·

    Exposing the Cost of Deep Learning Audio Development

    arXiv:2610.01619v1 Announce Type: cross Abstract: The environmental impact of deep learning has attracted increasing attention over the past decade. Existing studies mainly focus on the energy and carbon emissions of model training and inference, while the whole development phase…

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