Publications (8)
TypeFly: Flying Drones with Large Language Model
Guojun Chen, Xiaojing Yu, Neiwen Ling +1
Recent advancements in robot control using large language models (LLMs) have demonstrated significant potential, primarily due to LLMs' capabilities to understand natural language…
Soar: Design and Deployment of A Smart Roadside Infrastructure System for Autonomous Driving
Shuyao Shi, Neiwen Ling, Zhehao Jiang +9
Recently,smart roadside infrastructure (SRI) has demonstrated the potential of achieving fully autonomous driving systems. To explore the potential of infrastructure-assisted auton…
Timely Fusion of Surround Radar/Lidar for Object Detection in Autonomous Driving Systems
Wenjing Xie, Tao Hu, Neiwen Ling +3
Fusing Radar and Lidar sensor data can fully utilize their complementary advantages and provide more accurate reconstruction of the surrounding for autonomous driving systems. Surr…
TimelyLLM: Segmented LLM Serving System for Time-sensitive Robotic Applications
Neiwen Ling, Guojun Chen, Lin Zhong
Large Language Models (LLMs) such as GPT-4 and Llama3 can already comprehend complex commands and process diverse tasks. This advancement facilitates their application in controlli…
Moses: Efficient Exploitation of Cross-device Transferable Features for Tensor Program Optimization
Zhihe Zhao, Xian Shuai, Yang Bai +4
Achieving efficient execution of machine learning models has attracted significant attention recently. To generate tensor programs efficiently, a key component of DNN compilers is…
EdgeFM: Leveraging Foundation Model for Open-set Learning on the Edge
Bufang Yang, Lixing He, Neiwen Ling +5
Deep Learning (DL) models have been widely deployed on IoT devices with the help of advancements in DL algorithms and chips. However, the limited resources of edge devices make the…
Miriam: Exploiting Elastic Kernels for Real-time Multi-DNN Inference on Edge GPU
Zhihe Zhao, Neiwen Ling, Nan Guan +1
Many applications such as autonomous driving and augmented reality, require the concurrent running of multiple deep neural networks (DNN) that poses different levels of real-time p…
Diagnosing Training Inference Mismatch in LLM Reinforcement Learning
Tianle Zhong, Neiwen Ling, Yifan Pi +5
Modern LLM RL systems separate rollout generation from policy optimization. These two stages are expected to produce token probabilities that match exactly. However, implementation…