192 citations · 219 across the 4 of their papers we have counts for
4 papers
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…
SCALE-Sim: Systolic CNN Accelerator Simulator
Ananda Samajdar, Yuhao Zhu, Paul Whatmough +2
Systolic Arrays are one of the most popular compute substrates within Deep Learning accelerators today, as they provide extremely high efficiency for running dense matrix multiplic…
Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision
Yuhao Zhu, Anand Samajdar, Matthew Mattina +1
Continuous computer vision (CV) tasks increasingly rely on convolutional neural networks (CNN). However, CNNs have massive compute demands that far exceed the performance and energ…
Mobile Machine Learning Hardware at ARM: A Systems-on-Chip (SoC) Perspective
Yuhao Zhu, Matthew Mattina, Paul Whatmough
Machine learning is playing an increasingly significant role in emerging mobile application domains such as AR/VR, ADAS, etc. Accordingly, hardware architects have designed customi…