用电池做时间缓冲，让边缘AI更低碳运行
FM-CAC: Carbon-Aware Control for Battery-Buffered Edge AI via Time-Series Foundation Models
- 利用时序基础模型预测碳排放，动态调度任务与电池充放电
- 碳排放降低65.6%，推理准确率几乎不受影响
- 适合长期运行的边缘AI系统，尤其关注碳足迹的部署场景
随着边缘AI设备规模扩展至数十亿台并持续运行实时复合型人工智能流水线，其能耗和碳排放成为巨大且未受管控的来源。为在保障服务质量（QoS）的同时降低碳排放，本文提出FM-CAC，一种基于电池作为主动时间缓冲的前瞻性碳感知控制框架。通过解耦能源获取与消耗，FM-CAC可最大化使用低碳电力，显著减少碳排放。在每个控制步骤中，联合优化软件流水线变体、硬件工作点以及电池充放电动作。为支持该决策过程，FM-CAC采用边缘友好的时序基础模型（TSFMs）进行零样本碳排放预测，并将其融入具有延迟成本归因的动态规划求解器，以防止短期行为导致电池过早耗尽。实验结果表明，FM-CAC在保持近似最大推理准确率的同时，碳排放降低高达65.6%。
原文摘要 · Abstract (English)
As edge AI deployments scale to billions of devices running always-on, real-time compound AI pipelines, they represent a massive and largely unmanaged source of energy consumption and carbon emissions. To reduce carbon emissions while maximizing Quality-of-Service (QoS), this paper proposes FM-CAC, a proactive carbon-aware control framework that leverages a battery as an active temporal buffer. By decoupling energy acquisition from energy consumption, FM-CAC can maximize the use of low-carbon energy, substantially reducing carbon emissions. At each control step, FM-CAC jointly optimizes the software pipeline variant, the hardware operating point, and the battery charging and discharging actions. To support this decision process, FM-CAC leverages edge-friendly Time-Series Foundation Models (TSFMs) for zero-shot carbon forecasting and integrates these forecasts into a dynamic programming solver with deferred cost attribution to prevent myopic battery depletion. Results show that FM-CAC reduces carbon emissions by up to 65.6% while maintaining near-maximum inference accuracy.
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