3 papers
cs.LG2026
EnergyLens: Predictive Energy-Aware Exploration for Multi-GPU LLM Inference Optimization
Zhiye Song, Kyungmi Lee, Eun Kyung Lee +3
We present EnergyLens, an end-to-end framework for energy-aware large language model (LLM) inference optimization. As LLMs scale, predicting and reducing their energy footprint has…
cs.AR2026
EnergAIzer: Fast and Accurate GPU Power Estimation Framework for AI Workloads
Kyungmi Lee, Zhiye Song, Eun Kyung Lee +3
As AI workloads drive increases in datacenter power consumption, accurate GPU power estimation is critical for proactive power management. However, existing power models face a sca…
cs.DC2024
A Robust Power Model Training Framework for Cloud Native Runtime Energy Metric Exporter
Sunyanan Choochotkaew, Chen Wang, Huamin Chen +4
Estimating power consumption in modern Cloud environments is essential for carbon quantification toward green computing. Specifically, it is important to properly account for the p…