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

192 citations · 280 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2025

Residual Speech Embeddings for Tone Classification: Removing Linguistic Content to Enhance Paralinguistic Analysis

Hamdan Al Ahbabi, Gautier Marti, Saeed AlMarri +1

Self-supervised learning models for speech processing, such as wav2vec2, HuBERT, WavLM, and Whisper, generate embeddings that capture both linguistic and paralinguistic information…

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★ 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…