用全生命周期评估量化AI模型的资源消耗与环境影响
Evaluation of ML Resource Utilization Requires Model Life Cycle Assessment
- 提出将生命周期评估方法用于AI模型开发全流程
- 涵盖硬件制造到训练推理的全过程资源成本
- 适合关注可持续AI的研究者与政策制定者
准确核算人工智能系统所需的能源消耗和环境影响，对研究人员、开发者、政策制定者及用户评估大规模构建系统的障碍至关重要。随着AI开发与部署管道及其底层基础设施复杂性的增加，仅关注单次训练或单个推理成本的传统效率评估方法已不再足够。本文主张将生命周期评估（LCA）应用于机器学习模型开发与部署流程，以全面衡量所需资源及其下游影响。生命周期评估可整合AI系统及其基础架构在整个生命周期内的成本，包括硬件制造的隐含成本以及训练和推理阶段的运营成本。
原文摘要 · Abstract (English)
Proper accounting of the energy requirements and environmental impact of artificial intelligence (AI) systems is necessary for researchers, developers, policy makers, and users to assess the barriers to building systems at scale. With the growing complexity of pipelines and underlying infrastructure needed to develop and deploy AI systems, previous approaches for evaluating AI efficiency which focus on the costs of a single training run or an individual inference prediction are no longer sufficient. In this position paper, we enunciate the need for applying life cycle assessment to evaluate the costs of the machine learning model development and deployment pipeline to properly account for the required resources and downstream impact. Life cycle assessments enable the incorporation of costs across the full life cycle of an AI system and its underlying infrastructure, from the embodied costs associated with the physical computing hardware through the operational costs in training and inference.
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