3 papers
cs.AI2026
Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference
Mostafa Elhoushi, Alex Pretko, Nolan Dey +6
Layer dropout (a.k.a. stochastic depth) has been shown to enable faster training, higher accuracy, and robustness to zero-shot layer pruning in both language and vision transformer…
cs.AR2025
Modeling PFAS in Semiconductor Manufacturing to Quantify Trade-offs in Energy Efficiency and Environmental Impact of Computing Systems
Mariam Elgamal, Abdulrahman Mahmoud, Gu-Yeon Wei +2
The electronics and semiconductor industry is a prominent consumer of per- and poly-fluoroalkyl substances (PFAS), also known as forever chemicals. PFAS are persistent in the envir…
cs.CV2023
Hardware Resilience Properties of Text-Guided Image Classifiers
Syed Talal Wasim, Kabila Haile Soboka, Abdulrahman Mahmoud +3
This paper presents a novel method to enhance the reliability of image classification models during deployment in the face of transient hardware errors. By utilizing enriched text…