Deep learning applications at the network edge lead to a significant growth in AI-related carbon emissions, presenting a critical sustainability challenge. The existing edge computing frameworks optimize for latency and throughput, but they largely ignore the environmental impact of inference workloads. This paper introduces CarbonEdge, a carbon-aware deep learning inference framework that extends adaptive model partitioning with carbon footprint estimation and green scheduling apabilities. We propose a carbon-aware scheduling algorithm that extends traditional weighted scoring with a carbon efficiency metric, supporting a tunable performance--carbon trade-off (demonstrated via weight sweep). Experimental evaluations on Docker-simulated heterogeneous edge environments show that CarbonEdge-Green mode achieves a 22.9% reduction in carbon emissions compared to monolithic execution. The framework achieves 1.3x improvement in carbon efficiency (245.8 vs 189.5 inferences per gram CO2) with negligible scheduling overhead (0.03ms per task). These results highlight the framework's potential for sustainable edge AI deployment, providing researchers and practitioners a tool to quantify and minimize the environmental footprint of distributed deep learning inference.

翻译：网络边缘的深度学习应用导致与AI相关的碳排放显著增长，这构成了严峻的可持续性挑战。现有边缘计算框架优化延迟与吞吐量，却大多忽略了推理工作负载的环境影响。本文提出CarbonEdge——一种碳感知深度学习推理框架，通过集成碳足迹估算与绿色调度能力扩展了自适应模型分区机制。我们设计了一种碳感知调度算法，该算法在传统加权评分基础上引入碳效率指标，支持可调的性能-碳权衡（通过权重扫描验证）。在Docker模拟的异构边缘环境中的实验评估表明：相较于单体执行模式，CarbonEdge绿色模式实现了22.9%的碳排放减少；该框架在碳效率上取得1.3倍的提升（每克CO2对应245.8次推理vs 189.5次推理），且调度开销可忽略不计（每任务0.03毫秒）。这些结果凸显了该框架在可持续边缘AI部署中的潜力，为研究人员和从业者提供了量化并最小化分布式深度学习推理环境足迹的工具。