6 papers
The Anatomy of Silent Data Corruption: GPU Error Pattern Study and Modeling Guidance
Chung-Hsuan Tung, Yanxiang Huang, Nirmal Saxena +5
Silent data corruption (SDC) threatens the reliability of large-scale GPU clusters used for training large language models, yet its rarity and lack of explicit error signals make a…
LLM-PRISM: Characterizing Silent Data Corruption from Permanent GPU Faults in LLM Training
Abhishek Tyagi, Saurabh Hukerikar, Nirmal Saxena +4
Large-scale LLM training is increasingly susceptible to hardware defects stemming from manufacturing escapes and silicon aging. These defects manifest as Silent Data Corruption (SD…
Real-Time and Scalable Zak-OTFS Receiver Processing on GPUs
Junyao Zheng, Chung-Hsuan Tung, Yuncheng Yao +6
Orthogonal time frequency space (OTFS) modulation offers superior robustness to high-mobility channels compared to conventional orthogonal frequency-division multiplexing (OFDM) wa…
RISE: Real-time Image Processing for Spectral Energy Detection and Localization
Chung-Hsuan Tung, Zhenzhou Qi, Tingjun Chen
Energy detection is widely used for spectrum sensing, but accurately localizing the time and frequency occupation of signals in real-time for efficient spectrum sharing remains cha…
Nexus: Efficient and Scalable Multi-Cell mmWave Baseband Processing with Heterogeneous Compute
Zhenzhou Qi, Chung-Hsuan Tung, Zhihui Gao +1
The rapid adoption of 5G New Radio (NR), particularly in the millimeter-wave (mmWave) spectrum, imposes stringent demands on the flexibility, scalability, and efficiency of baseban…
DecodeX: Exploring and Benchmarking of LDPC Decoding across CPU, GPU, and ASIC Platforms
Zhenzhou Qi, Yuncheng Yao, Yiming Li +4
Emerging virtualized radio access networks (vRANs) demand flexible and efficient baseband processing across heterogeneous compute substrates. In this paper, we present DecodeX, a u…