Edge devices are often deployed at scale, yet their environmental impact, shaped by complex interactions between hardware choices, workload demands, power systems, and deployment context, has been overlooked by the mobile computing community. We present MicroGreen, a design-time framework that enables carbon-aware design for edge ML systems. By combining component-level carbon models with workload profiling and environment-aware energy analysis, MicroGreen identifies carbon-optimal configurations across diverse conditions. Our results show that the most energy-efficient processor is not always the most sustainable, and that ambient energy availability, inference rate, and deployment lifetime can shift the carbon-optimal design by over an order of magnitude. Through a real vision-based visitor detection and counting deployment in New York City parks, we demonstrate that heterogeneous, location-aware designs reduce total emissions by 47.72% compared to a homogeneous baseline.

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.