FM-CAC: Carbon-Aware Control for Battery-Buffered Edge AI via Time-Series Foundation Models
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 a...
| Authors | Kang Yang, Walid A. Hanafy, Prashant Shenoy et al. |
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| Primary category | eess.SY |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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