标题： 碳清晰度：理解并解决可持续计算中的隐含碳不确定性 显示英文标题

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

摘要： 由于其对计算领域碳排放的显著贡献，隐含碳足迹建模已成为一个日益受到关注的领域。 然而，现有模型的确定性未能解释半导体供应链的空间和时间变化。 缺乏不确定性建模限制了系统设计人员做出明智的、具有碳意识的决策的能力。 我们推出了CarbonClarity，这是一个概率框架，旨在通过反映不同技术节点间单位面积能耗、单位面积气体消耗、产量和碳强度不确定性的分布来建模隐含碳足迹。 我们的框架能够更深入地理解设计选择（例如芯片架构以及新旧技术节点的选择）如何影响排放及其相关的不确定性。 例如，我们表明，对于7nm技术节点，每平方厘米隐含碳的平均值和第95个百分位数之间的差距可能高达1.6倍。 此外，我们通过案例研究证明：（i）CarbonClarity 是设备配置的宝贵资源，有助于在紧张的碳预算下保持性能；（ii）小芯片技术和成熟节点不仅可以减少隐含碳，而且还显着降低其相关的不确定性，与移动应用程序的单片设计相比，第 95 个百分位数减少了 18%。 显示英文摘要

摘要： 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.