# FM-CAC: Carbon-Aware Control for Battery-Buffered Edge AI via Time-Series Foundation Models

arXiv: https://arxiv.org/abs/2604.16448 · HTML: https://mlsanity.com/paper/2604.16448/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 57 references that are in the map.
- First posted on arXiv 7 Apr 2026.

## Builds on (most cited first, 17 of 17)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Chronos: Learning the Language of Time Series](https://mlsanity.com/paper/2403.07815/) (2024)
- [Sundial: A Family of Highly Capable Time Series Foundation Models](https://mlsanity.com/paper/2502.00816/) (2025)
- [Carbon-Aware Computing for Datacenters](https://mlsanity.com/paper/2106.11750/) (2021)
- [CarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-Efficiency](https://mlsanity.com/paper/2302.08681/) (2023)
- [Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud](https://mlsanity.com/paper/2110.13234/) (2021)
- [GreenScale: Carbon-Aware Systems for Edge Computing](https://mlsanity.com/paper/2304.00404/) (2023)
- [DVFS-Aware DNN Inference on GPUs: Latency Modeling and Performance Analysis](https://mlsanity.com/paper/2502.06295/) (2025)
- [Deep Learning on Mobile Devices Through Neural Processing Units and Edge Computing](https://mlsanity.com/paper/2112.02439/) (2021)
- [Carbon-Aware EV Charging](https://mlsanity.com/paper/2209.12373/) (2022)
- [Benchmarking Spatiotemporal Reasoning in LLMs and Reasoning Models: Capabilities and Challenges](https://mlsanity.com/paper/2505.11618/) (2025)
- [EcoLife: Carbon-Aware Serverless Function Scheduling for Sustainable Computing](https://mlsanity.com/paper/2409.02085/) (2024)
- [CarbonX: An Open-Source Tool for Computational Decarbonization Using Time Series Foundation Models](https://mlsanity.com/paper/2510.01521/) (2025)
- [Spectral Predictability as a Fast Reliability Indicator for Time Series Forecasting Model Selection](https://mlsanity.com/paper/2511.08884/) (2025)
- [CarbonEdge: Leveraging Mesoscale Spatial Carbon-Intensity Variations for Low Carbon Edge Computing](https://mlsanity.com/paper/2502.14076/) (2025)
- [Energy-Aware Dynamic Neural Inference](https://mlsanity.com/paper/2411.02471/) (2024)
- [Online Smoothed Demand Management](https://mlsanity.com/paper/2511.18554/) (2025)

## Abstract (arXiv)

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.