2 citations · 3 across the 10 of their papers we have counts for
10 papers
GAPL: Grounded Action-effect Policy Learning for LLM-Based Trajectory Planning
Zhihong Cui, Hengyu Liu, Zhangkai Wu +5
Trajectory planning for autonomous driving requires both high-level reasoning and precise low-level control. Large Language Models (LLMs) offer semantic-rich planning capabilities,…
Empirical Analysis of GPU Frequency Behavior Under ML Workloads
Truong-Thanh Le, Hoang-Loc La, Amir Taherkordi +3
This work presents ongoing research on the frequency scaling behavior of NVIDIA GPUs when executing ML/AI workloads. Our preliminary findings show that, on lower-performance GPUs,…
E2LLM: Towards Efficient LLM Serving in Heterogeneous Edge/Fog Environments
Truong-Thanh Le, Amir Taherkordi, Hoang-Loc La +3
Large Language Models (LLMs) have become integral to modern applications, yet their deployment remains challenging. Beyond executing the models themselves, practical deployment mus…
C-TRAIL: A Commonsense World Framework for Trajectory Planning in Autonomous Driving
Zhihong Cui, Haoran Tang, Tianyi Li +4
Trajectory planning for autonomous driving increasingly leverages large language models (LLMs) for commonsense reasoning, yet LLM outputs are inherently unreliable, posing risks in…
PM2Lat: Highly Accurate and Generalized Prediction of DNN Execution Latency on GPUs
Truong-Thanh Le, Hoang-Loc La, Amir Taherkordi +3
We present PM2Lat, a fast and generalized framework for accurately predicting the latency of deep neural network models on GPUs, with special focus on NVIDIA. Unlike prior methods…
Personalized Federated Learning-Driven Beamforming Optimization for Integrated Sensing and Communication Systems
Zhou Ni, Sravan Reddy Chintareddy, Peiyuan Guan +1
In this paper, we propose an Expectation-Maximization-based (EM) Personalized Federated Learning (PFL) framework for multi-objective optimization (MOO) in Integrated Sensing and Co…
Cost Optimization for Serverless Edge Computing with Budget Constraints using Deep Reinforcement Learning
Chen Chen, Peiyuan Guan, Ziru Chen +3
Serverless computing adopts a pay-as-you-go billing model where applications are executed in stateless and shortlived containers triggered by events, resulting in a reduction of mo…
Variational autoencoder-based neural network model compression
Liang Cheng, Peiyuan Guan, Amir Taherkordi +2
Variational Autoencoders (VAEs), as a form of deep generative model, have been widely used in recent years, and shown great great peformance in a number of different domains, inclu…
Context-aware Container Orchestration in Serverless Edge Computing
Peiyuan Guan, Chen Chen, Ziru Chen +3
Adopting serverless computing to edge networks benefits end-users from the pay-as-you-use billing model and flexible scaling of applications. This paradigm extends the boundaries o…
Optimizing NOMA Transmissions to Advance Federated Learning in Vehicular Networks
Ziru Chen, Zhou Ni, Peiyuan Guan +4
Diverse critical data, such as location information and driving patterns, can be collected by IoT devices in vehicular networks to improve driving experiences and road safety. Howe…