35 citations · 35 across the 9 of their papers we have counts for
9 papers
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…
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…
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…
PlatformX: An End-to-End Transferable Platform for Energy-Efficient Neural Architecture Search
Xiaolong Tu, Dawei Chen, Kyungtae Han +2
Hardware-Aware Neural Architecture Search (HW-NAS) has emerged as a powerful tool for designing efficient deep neural networks (DNNs) tailored to edge devices. However, existing me…
lm-Meter: Unveiling Runtime Inference Latency for On-Device Language Models
Haoxin Wang, Xiaolong Tu, Hongyu Ke +3
Large Language Models (LLMs) are increasingly integrated into everyday applications, but their prevalent cloud-based deployment raises growing concerns around data privacy and long…
GreenAuto: An Automated Platform for Sustainable AI Model Design on Edge Devices
Xiaolong Tu, Dawei Chen, Kyungtae Han +2
We present GreenAuto, an end-to-end automated platform designed for sustainable AI model exploration, generation, deployment, and evaluation. GreenAuto employs a Pareto front-based…
DeepEn2023: Energy Datasets for Edge Artificial Intelligence
Xiaolong Tu, Anik Mallik, Haoxin Wang +1
Climate change poses one of the most significant challenges to humanity. As a result of these climatic changes, the frequency of weather, climate, and water-related disasters has m…
Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge Devices
Xiaolong Tu, Anik Mallik, Dawei Chen +4
Today, deep learning optimization is primarily driven by research focused on achieving high inference accuracy and reducing latency. However, the energy efficiency aspect is often…
Visualization of Mobility Digital Twin: Framework Design, Case Study, and Future Challenges
Yueyang Liu, Xiaolong Tu, Dawei Chen +4
A Mobility Digital Twin is an emerging implementation of digital twin technology in the transportation domain, which creates digital replicas for various physical mobility entities…