Life-Cycle Emissions of AI Hardware: A Cradle-To-Grave Approach and Generational Trends
It addresses the environmental sustainability problem for AI researchers and engineers by providing a detailed framework to assess and reduce the carbon footprint of specialized hardware, though it is incremental in building on existing LCA methods.
This study conducted the first comprehensive life-cycle assessment of greenhouse gas emissions for AI hardware accelerators, specifically analyzing five Tensor Processing Units (TPUs) across all stages from raw material extraction to disposal, and introduced a new metric called compute carbon intensity (CCI) that improved 3x from TPU v4i to TPU v6e.
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.