首次全面评估AI芯片全生命周期碳排放，揭示性能提升显著降低碳足迹。
Life-Cycle Emissions of AI Hardware: A Cradle-To-Grave Approach and Generational Trends
- 构建从原料到废弃的全流程碳排放模型，覆盖制造、使用与处置各阶段。
- TPU v4i 到 v6e 的计算碳强度下降3倍，体现代际技术进步的环保效益。
- 提出可复用的碳强度指标（CCI），助力硬件与软件协同优化可持续性。
专用硬件加速器推动人工智能快速发展，其能效直接影响AI的环境可持续性。本研究首次发布AI加速器全生命周期碳排放评估（LCA），包含首个公开的AI加速器制造阶段碳排放数据。通过对五代张量处理单元（TPUs）的分析，涵盖原材料开采、制造、部署、服务及模型训练推理过程中的能耗，利用第一手数据提供了迄今最全面的AI硬件环境影响评估。研究详细描述了LCA方法，旨在为计算机工程师提供教程、路线图和启发，以推动行业开展类似评估。研究衍生出新的度量指标‘计算碳强度’（CCI），有助于评估硬件可持续性并估算训练与推理的碳足迹。结果显示，从TPU v4i到v6e，CCI改善3倍。尽管本文聚焦硬件，但软件进步也进一步放大了这些减排成效。
原文摘要 · Abstract (English)
Specialized hardware accelerators aid the rapid advancement of artificial intelligence (AI), and their efficiency impacts AI's environmental sustainability. This study presents the first publication of a comprehensive AI accelerator life-cycle assessment (LCA) of greenhouse gas emissions, including the first publication of manufacturing emissions of an AI accelerator. Our analysis of five Tensor Processing Units (TPUs) encompasses all stages of the hardware lifespan - from raw material extraction, manufacturing, and disposal, to energy consumption during development, deployment, and serving of AI models. Using first-party data, it offers the most comprehensive evaluation to date of AI hardware's environmental impact. We include detailed descriptions of our LCA to act as a tutorial, road map, and inspiration for other computer engineers to perform similar LCAs to help us all understand the environmental impacts of our chips and of AI. A byproduct of this study is the new metric compute carbon intensity (CCI) that is helpful in evaluating AI hardware sustainability and in estimating the carbon footprint of training and inference. This study shows that CCI improves 3x from TPU v4i to TPU v6e. Moreover, while this paper's focus is on hardware, software advancements leverage and amplify these gains.
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