35 citations · 35 across the 9 of their papers we have counts for
Showing 2026Show all
3 papers · 1 filter
cs.CL2026
How Do Prompt Variations Affect Energy Consumption in On-Device LLMs?
Wei Hu, Xiaolong Tu, Dawei Chen +3
Large language models (LLMs) are increasingly deployed on mobile devices, making energy efficiency a key deployment constraint, yet the energy impact of prompt design remains under…
cs.AI2026
Transferable Latency Prediction for Fast LLM Screening on Heterogeneous Edge Devices
Xiaolong Tu, Vinod K. Mishra, Venkat R. Dasari +2
Accurate latency prediction is critical for deploying large language models (LLMs) on heterogeneous edge devices, where inference latency is affected by model architecture, prompt…
cs.AI2026
Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting
Ruiyi Tao, Xiaolong Tu, Haoxin Wang
Large Language Models (LLMs) are increasingly deployed on mobile and embedded devices to improve privacy and reduce network latency. Yet on-device inference faces a fundamental con…