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.

翻译：专用硬件加速器推动了人工智能（AI）的快速发展，其效率影响着AI的环境可持续性。本研究首次发布了关于AI加速器温室气体排放的全面生命周期评估（LCA），其中包括首次公开的AI加速器制造排放数据。我们对五款张量处理单元（TPU）的分析涵盖了硬件生命周期的所有阶段——从原材料提取、制造和废弃，到AI模型开发、部署和服务过程中的能源消耗。利用第一方数据，本研究提供了迄今为止对AI硬件环境影响最全面的评估。我们详细描述了LCA方法，旨在作为教程、路线图和启发，帮助其他计算机工程师开展类似的LCA研究，以共同理解芯片及AI的环境影响。本研究的副产品是提出了新的指标——计算碳强度（CCI），该指标有助于评估AI硬件可持续性，并估算训练和推理的碳足迹。研究表明，从TPU v4i到TPU v6e，CCI提升了3倍。此外，虽然本文聚焦于硬件，但软件进步进一步利用并放大了这些收益。