FM-CAC uses battery buffering and time-series foundation models for zero-shot carbon forecasting in a dynamic programming optimizer to reduce edge AI carbon emissions by up to 65.6% with near-maximum accuracy.
Energy- Aware Dynamic Neural Inference
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
eess.SY 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
The paper surveys energy efficiency strategies for Agentic AI inference by proposing a new accounting framework and taxonomy that spans model simplification, computation control, input optimization, and cross-layer co-design with wireless networks.
citing papers explorer
-
FM-CAC: Carbon-Aware Control for Battery-Buffered Edge AI via Time-Series Foundation Models
FM-CAC uses battery buffering and time-series foundation models for zero-shot carbon forecasting in a dynamic programming optimizer to reduce edge AI carbon emissions by up to 65.6% with near-maximum accuracy.
-
Networking-Aware Energy Efficiency in Agentic AI Inference: A Survey
The paper surveys energy efficiency strategies for Agentic AI inference by proposing a new accounting framework and taxonomy that spans model simplification, computation control, input optimization, and cross-layer co-design with wireless networks.