CarbonClarity: Understanding and Addressing Uncertainty in Embodied Carbon for Sustainable Computing

TL;DR

CarbonClarity introduces a probabilistic framework to model embodied carbon footprints in computing, accounting for uncertainties in supply chain variables, aiding sustainable design decisions with quantifiable risk assessments.

Contribution

It is the first to incorporate uncertainty modeling into embodied carbon footprint analysis, providing a probabilistic approach for more informed and resilient sustainable computing designs.

Findings

Embodied carbon variability can reach up to 1.6X between mean and 95th percentile for 7nm nodes.

CarbonClarity helps optimize device provisioning under tight carbon budgets.

Chiplet technology and mature nodes reduce both embodied carbon and its uncertainty, lowering the 95th percentile by 18%.

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 95th percentile of embodied carbon per cm$^2$ can reach up to 1.6X 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 95th percentile compared to monolithic designs for the mobile application.