发表机构
Centrale Med, Aix Marseille Univ; CNRS, LIS; Université de Lorraine, CNRS; Inria, LORIA（中央理工学院，艾克斯-马赛大学; 法国国家科学研究中心，信息与系统实验室; 洛林大学，法国国家科学研究中心; 法国国家信息与自动化研究所，洛林计算机科学实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于Grid5000日志估算深度学习音频项目开发阶段能耗的方法，发现开发能耗是训练最佳模型的3至256倍，呼吁全生命周期报告能耗。
AI 中文摘要
深度学习对环境的影响在过去十年中引起了越来越多的关注。现有研究主要关注模型训练和推理的能源消耗与碳排放，而整个开发阶段往往被忽视。然而，在这一阶段会进行架构原型设计和密集实验，这非常耗能。在本文中，我们提出了一种基于LORIA实验室使用的Grid5000共享计算平台活动日志来估算这些成本的方法。作为案例研究，我们聚焦于Multispeech研究团队开发的音频项目。我们评估了四个项目的总体能源成本，并将其与训练所报告模型的成本进行比较。我们的结果表明，开发阶段所需的能源是仅训练最佳性能模型的3至256倍。这些结果主张在基于深度学习的音频项目的整个生命周期中，更系统地报告能源消耗。
英文摘要
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 is often overlooked. Yet, architecture prototyping and intensive experiments are conducted during this stage, which is highly energy-demanding. In this article, we propose a methodology to estimate these costs, based on activity logs from the Grid5000 shared computing platform used by the LORIA laboratory. As a case-study, we focus on audio projects developed in the Multispeech research team. We evaluate the overall energy cost of four projects, and we compare them to those of training the reported models. Our results show that the energy required for the development phase is 3 to 256 times greater than that required to train the best-performing model alone. These results advocate for a more systematic reporting of energy consumption across the entire life cycle of deep learning-based audio projects.
Comments5 pages, 2 figures, 1 table
