动态调优边缘设备功耗，降低碳排放同时保障模型响应速度。
Ecomap: Sustainability-Driven Optimization of Multi-Tenant DNN Execution on Edge Servers
- 根据实时碳强度动态调整设备最大功耗
- 混合质量模型替换重负载模型，碳排放降30%、碳延迟产品降25%
- 适合关注绿色AI与边缘计算可持续性的研究者与工程师
边缘计算系统在并发运行多个深度神经网络（DNN）工作负载时，难以同时满足严格延迟要求、低功耗及环境可持续性。本文提出Ecomap框架，根据实时碳强度动态调节边缘设备最大功耗。该框架创新性引入混合质量模型，在延迟超限时自动用轻量级模型替代重型模型，确保服务响应性且仅轻微损失精度。同时采用基于Transformer的估计算法优化工作负载调度。在NVIDIA Jetson AGX Xavier上的实验表明，Ecomap平均减少30%碳排放，碳延迟产品（CDP）降低25%，同时保持与现有方法相当或更优的延迟和功耗效率。
原文摘要 · Abstract (English)
Edge computing systems struggle to efficiently manage multiple concurrent deep neural network (DNN) workloads while meeting strict latency requirements, minimizing power consumption, and maintaining environmental sustainability. This paper introduces Ecomap, a sustainability-driven framework that dynamically adjusts the maximum power threshold of edge devices based on real-time carbon intensity. Ecomap incorporates the innovative use of mixed-quality models, allowing it to dynamically replace computationally heavy DNNs with lighter alternatives when latency constraints are violated, ensuring service responsiveness with minimal accuracy loss. Additionally, it employs a transformer-based estimator to guide efficient workload mappings. Experimental results using NVIDIA Jetson AGX Xavier demonstrate that Ecomap reduces carbon emissions by an average of 30% and achieves a 25% lower carbon delay product (CDP) compared to state-of-the-art methods, while maintaining comparable or better latency and power efficiency.
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