18,811 research outputs found
Tensor Computation: A New Framework for High-Dimensional Problems in EDA
Many critical EDA problems suffer from the curse of dimensionality, i.e. the
very fast-scaling computational burden produced by large number of parameters
and/or unknown variables. This phenomenon may be caused by multiple spatial or
temporal factors (e.g. 3-D field solvers discretizations and multi-rate circuit
simulation), nonlinearity of devices and circuits, large number of design or
optimization parameters (e.g. full-chip routing/placement and circuit sizing),
or extensive process variations (e.g. variability/reliability analysis and
design for manufacturability). The computational challenges generated by such
high dimensional problems are generally hard to handle efficiently with
traditional EDA core algorithms that are based on matrix and vector
computation. This paper presents "tensor computation" as an alternative general
framework for the development of efficient EDA algorithms and tools. A tensor
is a high-dimensional generalization of a matrix and a vector, and is a natural
choice for both storing and solving efficiently high-dimensional EDA problems.
This paper gives a basic tutorial on tensors, demonstrates some recent examples
of EDA applications (e.g., nonlinear circuit modeling and high-dimensional
uncertainty quantification), and suggests further open EDA problems where the
use of tensor computation could be of advantage.Comment: 14 figures. Accepted by IEEE Trans. CAD of Integrated Circuits and
System
Colored noise in oscillators. Phase-amplitude analysis and a method to avoid the Ito-Stratonovich dilemma
We investigate the effect of time-correlated noise on the phase fluctuations
of nonlinear oscillators. The analysis is based on a methodology that
transforms a system subject to colored noise, modeled as an Ornstein-Uhlenbeck
process, into an equivalent system subject to white Gaussian noise. A
description in terms of phase and amplitude deviation is given for the
transformed system. Using stochastic averaging technique, the equations are
reduced to a phase model that can be analyzed to characterize phase noise. We
find that phase noise is a drift-diffusion process, with a noise-induced
frequency shift related to the variance and to the correlation time of colored
noise. The proposed approach improves the accuracy of previous phase reduced
models
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