CarbonClarity: Understanding and Addressing Uncertainty in Embodied Carbon for Sustainable Computing
Year: 2025
Type: conference-paper
Abstract: Embodied carbon footprint modeling has become an area of growing interest due to its significant contribution to carbon emissions in computing. However, the deterministic nature of the existing models fail to account for the spatial and temporal variability in the semiconductor supply chain. The absence of uncertainty modeling limits system designers’ ability to make informed, carbon-aware decisions. We introduce CarbonClarity, a probabilistic framework designed to model embodied carbon footprints through distributions that reflect uncertainties in energy-per-area, gas-per-area, yield, and carbon intensity across different technology nodes. Our framework enables a deeper understanding of how design choices, such as chiplet architectures and new vs. old technology node selection, impact emissions and their associated uncertainties. For example, we show that the gap between the mean and 95thpercentile of embodied carbon per cm2can reach up to 1.6× for the 7nm technology node. Additionally, we demonstrate through case studies that: (i) CarbonClarity is a valuable resource for device provisioning, help maintaining performance under a tight carbon budget; and (ii) chiplet technology and mature nodes not only reduce embodied carbon but also significantly lower its associated uncertainty, achieving an 18% reduction in the 95thpercentile compared to monolithic designs for the mobile application.
Authors: Xuesi Chen, Leo Han, Anvita Bhagavathula, Udit Gupta
Institution: Cornell Tech
Language: English
Topic: Green IT and Sustainability
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W4417182628