22,761 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
Reduced-order Description of Transient Instabilities and Computation of Finite-Time Lyapunov Exponents
High-dimensional chaotic dynamical systems can exhibit strongly transient
features. These are often associated with instabilities that have finite-time
duration. Because of the finite-time character of these transient events, their
detection through infinite-time methods, e.g. long term averages, Lyapunov
exponents or information about the statistical steady-state, is not possible.
Here we utilize a recently developed framework, the Optimally Time-Dependent
(OTD) modes, to extract a time-dependent subspace that spans the modes
associated with transient features associated with finite-time instabilities.
As the main result, we prove that the OTD modes, under appropriate conditions,
converge exponentially fast to the eigendirections of the Cauchy--Green tensor
associated with the most intense finite-time instabilities. Based on this
observation, we develop a reduced-order method for the computation of
finite-time Lyapunov exponents (FTLE) and vectors. In high-dimensional systems,
the computational cost of the reduced-order method is orders of magnitude lower
than the full FTLE computation. We demonstrate the validity of the theoretical
findings on two numerical examples
- …