activity
20142023
most citedEfficient Neural Network Robustness Certification with General Activation Functions

332 citations · 1.2k across the 32 of their papers we have counts for

collaborators
Showing 2014 · cs.CEShow all

6 papers · 2 filters

cs.CE2014★ 192 cited

Stochastic Testing Method for Transistor-Level Uncertainty Quantification Based on Generalized Polynomial Chaos

Zheng Zhang, Tarek A. El-Moselhy, Ibrahim +2

Uncertainties have become a major concern in integrated circuit design. In order to avoid the huge number of repeated simulations in conventional Monte Carlo flows, this paper pres…

cs.CE2014★ 36 cited

Calculation of Generalized Polynomial-Chaos Basis Functions and Gauss Quadrature Rules in Hierarchical Uncertainty Quantification

Zheng Zhang, Tarek A. El-Moselhy, Ibrahim +2

Stochastic spectral methods are efficient techniques for uncertainty quantification. Recently they have shown excellent performance in the statistical analysis of integrated circui…

cs.CE2014★ 17 cited

Efficient Uncertainty Quantification for the Periodic Steady State of Forced and Autonomous Circuits

Zheng Zhang, Tarek A. El-Moselhy, Paolo Maffezzoni +3

This brief paper proposes an uncertainty quantification method for the periodic steady-state (PSS) analysis with both Gaussian and non-Gaussian variations. Our stochastic testing f…

cs.CE2014

Uncertainty Quantification for Integrated Circuits: Stochastic Spectral Methods

Zheng Zhang, Ibrahim, M. Elfadel +1

Due to significant manufacturing process variations, the performance of integrated circuits (ICs) has become increasingly uncertain. Such uncertainties must be carefully quantified…

cs.CE2014★ 35 cited

Stochastic Testing Simulator for Integrated Circuits and MEMS: Hierarchical and Sparse Techniques

Zheng Zhang, Xiu Yang, Giovanni Marucci +5

Process variations are a major concern in today's chip design since they can significantly degrade chip performance. To predict such degradation, existing circuit and MEMS simulato…

cs.CE2014★ 108 cited

Enabling High-Dimensional Hierarchical Uncertainty Quantification by ANOVA and Tensor-Train Decomposition

Zheng Zhang, Xiu Yang, Ivan V. Oseledets +2

Hierarchical uncertainty quantification can reduce the computational cost of stochastic circuit simulation by employing spectral methods at different levels. This paper presents an…