35 citations · 35 across the 8 of their papers we have counts for
8 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…
AdaMap: High-Scalable Real-Time Cooperative Perception at the Edge
Qiang Liu, Yongjie Xue, Yuru Zhang +2
Cooperative perception is the key approach to augment the perception of connected and automated vehicles (CAVs) toward safe autonomous driving. However, it is challenging to achiev…
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…