35 citations · 35 across the 7 of their papers we have counts for
7 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…
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…
CAVE: Crowdsourcing Passing-By Vehicles for Reliable In-Vehicle Edge Computing
Jiahe Cao, Qiang Liu, Dawei Chen +1
In-vehicle edge computing is a much anticipated paradigm to serve ever-increasing computation demands originated from the ego vehicle, such as passenger entertainments. In this pap…
Unleashing the True Power of Age-of-Information: Service Aggregation in Connected and Autonomous Vehicles
Anik Mallik, Dawei Chen, Kyungtae Han +2
Connected and autonomous vehicles (CAVs) rely heavily upon time-sensitive information update services to ensure the safety of people and assets, and satisfactory entertainment appl…
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…