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

2025-07-01

AI Summary

Existing implicit carbon footprint models neglect spatiotemporal variability—across process nodes, geographies, and time—in the semiconductor supply chain and lack uncertainty quantification, thereby hindering carbon-aware design decisions. This paper introduces CarbonClarity, the first probabilistic carbon footprint modeling framework targeting compute device manufacturing. It systematically integrates spatiotemporal heterogeneity in energy density, specialty gas consumption, yield, and regional grid carbon intensity. CarbonClarity reveals that design choices—such as process node and architecture—significantly affect the full carbon emission distribution, not merely its mean: at the 7 nm node, the 95th-percentile implicit carbon is 1.6× higher than the mean. Case studies demonstrate that chiplet-based integration and mature-node fabrication reduce high-risk (i.e., tail) carbon emissions by up to 18%.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Security and Privacy: Data transparency and provenance
• Responsible Web: Sustainability and climate impact of web technologies

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.

Problem

• Modeling uncertainty in embodied carbon for computing systems
• Addressing spatial and temporal variability in semiconductor supply chains
• Enabling carbon-aware decisions for sustainable chip design

Innovation

• Probabilistic framework models embodied carbon uncertainties
• Incorporates variability in energy, gas, yield, carbon intensity
• Optimizes chiplet and node choices for lower emissions