CarbonEdge让边缘计算在推理时自动降低碳排放，兼顾效率与环保。
CarbonEdge: Carbon-Aware Deep Learning Inference Framework for Sustainable Edge Computing
- 基于碳足迹估算和绿色调度，动态分配模型任务
- 绿色模式下碳排放减少22.9%，碳效率提升30%
- 适合关注可持续边缘AI的开发者与研究者
边缘端的深度学习应用导致人工智能相关碳排放显著上升，构成严峻的可持续性挑战。现有边缘计算框架侧重延迟与吞吐优化，却忽视推理任务的环境影响。本文提出CarbonEdge——一种面向碳排放感知的深度学习推理框架，通过扩展自适应模型划分，引入碳足迹估算与绿色调度能力。设计了融合碳效率指标的调度算法，在权重扫描实验中实现可调的性能-碳排放权衡。在Docker模拟的异构边缘环境中测试表明，CarbonEdge-Green模式相比单体执行减少22.9%碳排放；碳效率达245.8次推理/克CO2（较基准提升1.3倍），调度开销极低（每任务0.03ms）。结果验证了该框架在可持续边缘智能部署中的潜力，为研究者与实践者提供量化与降低分布式推理碳足迹的工具。
原文摘要 · Abstract (English)
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.
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