3 citations · 4 across the 4 of their papers we have counts for
5 papers
Analysis of LLM Vulnerability to GPU Soft Errors: An Instruction-Level Fault Injection Study
Duo Chai, Zizhen Liu, Shuhuai Wang +4
Large language models (LLMs) are highly compute- and memory-intensive, posing significant demands on high-performance GPUs. At the same time, advances in GPU technology driven by s…
DENet: Dual-Path Edge Network with Global-Local Attention for Infrared Small Target Detection
Jiayi Zuo, Songwei Pei, Qian Li
Infrared small target detection is crucial for remote sensing applications like disaster warning and maritime surveillance. However, due to the lack of distinctive texture and morp…
GroupNL: Low-Resource and Robust CNN Design over Cloud and Device
Chuntao Ding, Jianhang Xie, Junna Zhang +3
Deploying Convolutional Neural Network (CNN) models on ubiquitous Internet of Things (IoT) devices in a cloud-assisted manner to provide users with a variety of high-quality servic…
LoRA-C: Parameter-Efficient Fine-Tuning of Robust CNN for IoT Devices
Chuntao Ding, Xu Cao, Jianhang Xie +3
Efficient fine-tuning of pre-trained convolutional neural network (CNN) models using local data is essential for providing high-quality services to users using ubiquitous and resou…
LLM-Based Misconfiguration Detection for AWS Serverless Computing
Jinfeng Wen, Zhenpeng Chen, Federica Sarro +4
Serverless computing is an emerging cloud computing paradigm that enables developers to build applications at the function level, known as serverless applications. Amazon Web Servi…