185 citations · 228 across the 21 of their papers we have counts for
22 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…
Hierarchical Federated Transfer Learning in Digital Twin-Based Vehicular Networks
Qasim Zia, Saide Zhu, Haoxin Wang +2
In recent research on the Digital Twin-based Vehicular Ad hoc Network(DT-VANET), Federated Learning (FL) has shown its ability to provide data privacy. However, Federated learning…
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…
Deformba: Vision State Space Model with Adaptive State Fusion
Hongyu Ke, Jack Morris, Yongkang Liu +4
State Space Models (SSMs) have emerged as a powerful and efficient alternative to Transformers, demonstrating linear-time complexity and exceptional sequence modeling capabilities.…
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…