2 citations · 4 across the 11 of their papers we have counts for
11 papers
PHOENIX: Resilient LLM Training with Hot-Swapping via Zero-Overhead Checkpoint
Haotian Xie, Junlin Chen, Mingkai Zheng +2
State-of-the-art large language model (LLM) training takes tens of thousands of graphics processing units (GPUs) for months and encounters failures across the software and hardware…
Understanding the Landscape of Ampere GPU Memory Errors
Zhu Zhu, Yu Sun, Dhatri Parakal +9
Graphics Processing Units (GPUs) have become a de facto solution for accelerating high-performance computing (HPC) applications. Understanding their memory error behavior is an ess…
FediLoRA: Practical Federated Fine-Tuning of Foundation Models Under Missing-Modality Constraints
Lishan Yang, Wei Emma Zhang, Nam Kha Nguygen +4
Federated Learning with LoRA fine-tuning offers an efficient and privacy-aware solution for institutions to collaboratively leverage their large datasets to train VLLMs. However, p…
FedDPG: An Adaptive Yet Efficient Prompt-tuning Approach in Federated Learning Settings
Ali Shakeri, Wei Emma Zhang, Amin Beheshti +3
Pre-trained Language Models (PLMs) have demonstrated impressive performance in various NLP tasks. However, traditional fine-tuning methods for leveraging PLMs for downstream tasks…
MMiC: Mitigating Modality Incompleteness in Clustered Federated Learning
Lishan Yang, Wei Emma Zhang, Quan Z. Sheng +3
In the era of big data, data mining has become indispensable for uncovering hidden patterns and insights from vast and complex datasets. The integration of multimodal data sources…
Safety Interventions against Adversarial Patches in an Open-Source Driver Assistance System
Cheng Chen, Grant Xiao, Daehyun Lee +4
Drivers are becoming increasingly reliant on advanced driver assistance systems (ADAS) as autonomous driving technology becomes more popular and developed with advanced safety feat…
PrisonBreak: Jailbreaking Large Language Models with at Most Twenty-Five Targeted Bit-flips
Zachary Coalson, Jeonghyun Woo, Chris S. Lin +8
We study a new vulnerability in commercial-scale safety-aligned large language models (LLMs): their refusal to generate harmful responses can be broken by flipping only a few bits…
Strategic Safety-Critical Attacks Against an Advanced Driver Assistance System
Xugui Zhou, Anna Schmedding, Haotian Ren +4
A growing number of vehicles are being transformed into semi-autonomous vehicles (Level 2 autonomy) by relying on advanced driver assistance systems (ADAS) to improve the driving e…
Enabling Software Resilience in GPGPU Applications via Partial Thread Protection
Lishan Yang, Bin Nie, Adwait Jog +1
Graphics Processing Units (GPUs) are widely used by various applications in a broad variety of fields to accelerate their computation but remain susceptible to transient hardware f…
Characterizing the COVID-19 Transmission in South Korea Using the KCDC Patient Data
Anna Schmedding, Lishan Yang, Riccardo Pinciroli +1
As the COVID-19 outbreak evolves around the world, the World Health Organization (WHO) and its Member States have been heavily relying on staying at home and lock down measures to…
The Life and Death of SSDs and HDDs: Similarities, Differences, and Prediction Models
Riccardo Pinciroli, Lishan Yang, Jacob Alter +1
Data center downtime typically centers around IT equipment failure. Storage devices are the most frequently failing components in data centers. We present a comparative study of ha…