activity
20172019
most citedOptimizing Multi-GPU Parallelization Strategies for Deep Learning Training

6 citations · 10 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2019★ 6 cited

Optimizing Multi-GPU Parallelization Strategies for Deep Learning Training

Saptadeep Pal, Eiman Ebrahimi, Arslan Zulfiqar +5

Deploying deep learning (DL) models across multiple compute devices to train large and complex models continues to grow in importance because of the demand for faster and more freq…

cs.CR2018★ 2 cited

Implementation and Analysis of Stable PUFs Using Gate Oxide Breakdown

Wei-Che Wang, Yair Yona, Yizhang Wu +2

We implement and analyze highly stable PUFs using two random gate oxide breakdown mechanisms: plasma induced breakdown and voltage stressed breakdown. These gate oxide breakdown PU…

cs.OH2017

Hotspot-aware DSA Grouping and Mask Assignment

Yasmine Badr, Puneet Gupta

In Directed Self Assembly (DSA), poor printing of guiding templates can cause misassembly resulting in high defect probability. Therefore, hotspots should be avoided in the choice…

cs.AR2017★ 2 cited

UNBIAS PUF: A Physical Implementation Bias Agnostic Strong PUF

Wei-Che Wang, Zhuoqi Li, Joseph Skudlarek +3

The Physical Unclonable Function (PUF) is a promising hardware security primitive because of its inherent uniqueness and low cost. To extract the device-specific variation from del…

cs.CR2017

Design and Analysis of Stability-Guaranteed PUFs

Wei-Che Wang, Yair Yona, Suhas Diggavi +1

The lack of stability is one of the major limitations that constrains PUF from being put in widespread practical use. In this paper, we propose a weak PUF and a strong PUF that are…