6,388 research outputs found
A Hierarchical Spatio-Temporal Statistical Model Motivated by Glaciology
In this paper, we extend and analyze a Bayesian hierarchical spatio-temporal
model for physical systems. A novelty is to model the discrepancy between the
output of a computer simulator for a physical process and the actual process
values with a multivariate random walk. For computational efficiency, linear
algebra for bandwidth limited matrices is utilized, and first-order emulator
inference allows for the fast emulation of a numerical partial differential
equation (PDE) solver. A test scenario from a physical system motivated by
glaciology is used to examine the speed and accuracy of the computational
methods used, in addition to the viability of modeling assumptions. We conclude
by discussing how the model and associated methodology can be applied in other
physical contexts besides glaciology.Comment: Revision accepted for publication by the Journal of Agricultural,
Biological, and Environmental Statistic
Pattern representation and recognition with accelerated analog neuromorphic systems
Despite being originally inspired by the central nervous system, artificial
neural networks have diverged from their biological archetypes as they have
been remodeled to fit particular tasks. In this paper, we review several
possibilites to reverse map these architectures to biologically more realistic
spiking networks with the aim of emulating them on fast, low-power neuromorphic
hardware. Since many of these devices employ analog components, which cannot be
perfectly controlled, finding ways to compensate for the resulting effects
represents a key challenge. Here, we discuss three different strategies to
address this problem: the addition of auxiliary network components for
stabilizing activity, the utilization of inherently robust architectures and a
training method for hardware-emulated networks that functions without perfect
knowledge of the system's dynamics and parameters. For all three scenarios, we
corroborate our theoretical considerations with experimental results on
accelerated analog neuromorphic platforms.Comment: accepted at ISCAS 201
Hierarchical approach to 'atomistic' 3-D MOSFET simulation
We present a hierarchical approach to the 'atomistic' simulation of aggressively scaled sub-0.1-μm MOSFETs. These devices are so small that their characteristics depend on the precise location of dopant atoms within them, not just on their average density. A full-scale three-dimensional drift-diffusion atomistic simulation approach is first described and used to verify more economical, but restricted, options. To reduce processor time and memory requirements at high drain voltage, we have developed a self-consistent option based on a solution of the current continuity equation restricted to a thin slab of the channel. This is coupled to the solution of the Poisson equation in the whole simulation domain in the Gummel iteration cycles. The accuracy of this approach is investigated in comparison to the full self-consistent solution. At low drain voltage, a single solution of the nonlinear Poisson equation is sufficient to extract the current with satisfactory accuracy. In this case, the current is calculated by solving the current continuity equation in a drift approximation only, also in a thin slab containing the MOSFET channel. The regions of applicability for the different components of this hierarchical approach are illustrated in example simulations covering the random dopant-induced threshold voltage fluctuations, threshold voltage lowering, threshold voltage asymmetry, and drain current fluctuations
Astrophysics on the GRAPE Family of Special Purpose Computers
The GRAPE-4, the world's fastest computer in 1995-1997, has produced some
major scientific results, through a wide diversity of large-scale simulations
in astrophysics. Applications have ranged from planetary formation, through the
evolution of star clusters and galactic nuclei, to the formation of galaxies
and clusters of galaxies.Comment: 15 pages, to apper in Scienc
EZ-AG: Structure-free data aggregation in MANETs using push-assisted self-repelling random walks
This paper describes EZ-AG, a structure-free protocol for duplicate
insensitive data aggregation in MANETs. The key idea in EZ-AG is to introduce a
token that performs a self-repelling random walk in the network and aggregates
information from nodes when they are visited for the first time. A
self-repelling random walk of a token on a graph is one in which at each step,
the token moves to a neighbor that has been visited least often. While
self-repelling random walks visit all nodes in the network much faster than
plain random walks, they tend to slow down when most of the nodes are already
visited. In this paper, we show that a single step push phase at each node can
significantly speed up the aggregation and eliminate this slow down. By doing
so, EZ-AG achieves aggregation in only O(N) time and messages. In terms of
overhead, EZ-AG outperforms existing structure-free data aggregation by a
factor of at least log(N) and achieves the lower bound for aggregation message
overhead. We demonstrate the scalability and robustness of EZ-AG using ns-3
simulations in networks ranging from 100 to 4000 nodes under different mobility
models and node speeds. We also describe a hierarchical extension for EZ-AG
that can produce multi-resolution aggregates at each node using only O(NlogN)
messages, which is a poly-logarithmic factor improvement over existing
techniques
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
Speeding-up the decision making of a learning agent using an ion trap quantum processor
We report a proof-of-principle experimental demonstration of the quantum
speed-up for learning agents utilizing a small-scale quantum information
processor based on radiofrequency-driven trapped ions. The decision-making
process of a quantum learning agent within the projective simulation paradigm
for machine learning is implemented in a system of two qubits. The latter are
realized using hyperfine states of two frequency-addressed atomic ions exposed
to a static magnetic field gradient. We show that the deliberation time of this
quantum learning agent is quadratically improved with respect to comparable
classical learning agents. The performance of this quantum-enhanced learning
agent highlights the potential of scalable quantum processors taking advantage
of machine learning.Comment: 21 pages, 7 figures, 2 tables. Author names now spelled correctly;
sections rearranged; changes in the wording of the manuscrip
Analytical Rescaling of Polymer Dynamics from Mesoscale Simulations
We present a theoretical approach to scale the artificially fast dynamics of
simulated coarse-grained polymer liquids down to its realistic value. As
coarse-graining affects entropy and dissipation, two factors enter the
rescaling: inclusion of intramolecular vibrational degrees of freedom, and
rescaling of the friction coefficient. Because our approach is analytical, it
is general and transferable. Translational and rotational diffusion of
unentangled and entangled polyethylene melts, predicted from mesoscale
simulations of coarse-grained polymer melts using our rescaling procedure, are
in quantitative agreement with united atom simulations and with experiments.Comment: 6 pages, 2 figures, 2 table
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