CarbonEdge: Carbon-Aware Deep Learning Inference Framework for Sustainable Edge Computing

1. 1Implement adaptive model partitioning to optimize inference workloads
2. 2Estimate the carbon footprint of different models and hardware configurations
3. 3Use CarbonEdge to select the most carbon-efficient model and hardware combination
4. 4Monitor and adjust the framework to ensure ongoing sustainability

AI engineers and data scientists on a team can benefit from CarbonEdge as it helps reduce the environmental impact of their models, while product managers can use it to improve the sustainability of their products

💡 CarbonEdge optimizes deep learning inference for low carbon emissions by extending adaptive model partitioning with carbon footprint estimation

💡 Reduce AI's carbon footprint with CarbonEdge, a sustainable edge computing framework