The authors propose models for both temporal and spatial locality of reference in streams of
requests arriving at Web servers. They show that simple models based on document …

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

Purpose–Process variation has become a major concern in the design of many nanometer
circuits, including interconnect pipelines. The purpose of this paper is to provide a …

This paper presents a novel linear regression-based polynomial chaos (PC) approach for
the efficient multidimensional uncertainty quantification of general distributed and lumped …

Accurately estimating congestion for proper global adaptive routing decisions (ie, determine
whether a packet should be routed minimally or non-minimally) has a significant impact on …

Stochastic spectral methods have achieved a great success in the uncertainty quantification
of many engineering problems, including variation-aware electronic and photonic design …

This article introduces a probabilistic machine learning framework for the uncertainty
quantification (UQ) of electronic circuits based on the Gaussian process regression (GPR) …

For a class of large linear input-output systems, we present a new model order reduction
algorithm based on general orthogonal polynomials in the time domain. The main idea of …

This paper presents a new Hermite-based approach to circuit variability analysis using the
polynomial-chaos (PC) paradigm. The new approach is aimed at limiting the growth of the …

This paper describes a new approach to extend the variability analysis based on the
polynomial chaos (PC) technique to nonlinear circuits. The proposed approach enables …