5 papers
RLHFSpec: Breaking the Efficiency Bottleneck in RLHF Training via Adaptive Drafting
Siqi Wang, Hailong Yang, Junjie Zhu +3
Reinforcement Learning from Human Feedback (RLHF) is an important fine-tuning technique for large language models (LLMs) and comprises three stages: generation, inference, and trai…
PRAGMA: A Profiling-Reasoned Multi-Agent Framework for Automatic Kernel Optimization
Kelun Lei, Hailong Yang, Huaitao Zhang +5
Designing high-performance kernels requires expert-level tuning and a deep understanding of hardware characteristics. Recent advances in large language models (LLMs) have enabled a…
LOw-cOst yet High-Performant Sparse Matrix-Matrix Multiplication on Arm SME Architectures
Kelun Lei, Hailong Yang, Kaige Zhang +8
Sparse matrix-dense matrix multiplication (SpMM) is a critical kernel in both scientific computing and emerging graph learning workloads. The recent Armv9 architecture introduces S…
Beyond Window-Based Detection: A Graph-Centric Framework for Discrete Log Anomaly Detection
Jiaxing Qi, Chang Zeng, Zhongzhi Luan +5
Detecting anomalies in discrete event logs is critical for ensuring system reliability, security, and efficiency. Traditional window-based methods for log anomaly detection often s…
Quantum Machine Learning in Log-based Anomaly Detection: Challenges and Opportunities
Jiaxing Qi, Chang Zeng, Zhongzhi Luan +6
Log-based anomaly detection (LogAD) is the main component of Artificial Intelligence for IT Operations (AIOps), which can detect anomalous that occur during the system on-the-fly.…