340 research outputs found
Physical parameter-aware Networks-on-Chip design
PhD ThesisNetworks-on-Chip (NoCs) have been proposed as a scalable, reliable
and power-efficient communication fabric for chip multiprocessors
(CMPs) and multiprocessor systems-on-chip (MPSoCs). NoCs determine
both the performance and the reliability of such systems, with a
significant power demand that is expected to increase due to developments
in both technology and architecture. In terms of architecture, an
important trend in many-core systems architecture is to increase the
number of cores on a chip while reducing their individual complexity.
This trend increases communication power relative to computation
power. Moreover, technology-wise, power-hungry wires are dominating
logic as power consumers as technology scales down. For these
reasons, the design of future very large scale integration (VLSI) systems
is moving from being computation-centric to communication-centric.
On the other hand, chip’s physical parameters integrity, especially
power and thermal integrity, is crucial for reliable VLSI systems. However,
guaranteeing this integrity is becoming increasingly difficult with
the higher scale of integration due to increased power density and operating
frequencies that result in continuously increasing temperature
and voltage drops in the chip. This is a challenge that may prevent
further shrinking of devices. Thus, tackling the challenge of power
and thermal integrity of future many-core systems at only one level
of abstraction, the chip and package design for example, is no longer
sufficient to ensure the integrity of physical parameters. New designtime
and run-time strategies may need to work together at different
levels of abstraction, such as package, application, network, to provide
the required physical parameter integrity for these large systems. This
necessitates strategies that work at the level of the on-chip network
with its rising power budget.
This thesis proposes models, techniques and architectures to improve
power and thermal integrity of Network-on-Chip (NoC)-based
many-core systems. The thesis is composed of two major parts: i)
minimization and modelling of power supply variations to improve
power integrity; and ii) dynamic thermal adaptation to improve thermal
integrity. This thesis makes four major contributions. The first is
a computational model of on-chip power supply variations in NoCs.
The proposed model embeds a power delivery model, an NoC activity
simulator and a power model. The model is verified with SPICE simulation
and employed to analyse power supply variations in synthetic
and real NoC workloads. Novel observations regarding power supply
noise correlation with different traffic patterns and routing algorithms
are found. The second is a new application mapping strategy aiming
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to minimize power supply noise in NoCs. This is achieved by defining
a new metric, switching activity density, and employing a force-based
objective function that results in minimizing switching density. Significant
reductions in power supply noise (PSN) are achieved with a low
energy penalty. This reduction in PSN also results in a better link timing
accuracy. The third contribution is a new dynamic thermal-adaptive
routing strategy to effectively diffuse heat from the NoC-based threedimensional
(3D) CMPs, using a dynamic programming (DP)-based distributed
control architecture. Moreover, a new approach for efficient extension
of two-dimensional (2D) partially-adaptive routing algorithms
to 3D is presented. This approach improves three-dimensional networkon-
chip (3D NoC) routing adaptivity while ensuring deadlock-freeness.
Finally, the proposed thermal-adaptive routing is implemented in
field-programmable gate array (FPGA), and implementation challenges,
for both thermal sensing and the dynamic control architecture are addressed.
The proposed routing implementation is evaluated in terms
of both functionality and performance.
The methodologies and architectures proposed in this thesis open a
new direction for improving the power and thermal integrity of future
NoC-based 2D and 3D many-core architectures
Time Domain SAR Processing with GPUs for Airborne Platforms
A time-domain backprojection processor for airborne synthetic aperture radar (SAR) has been developed at the University of Massachusetts’ Microwave Remote Sensing Lab (MIRSL). The aim of this work is to produce a SAR processor capable of addressing the motion compensation issues faced by frequency-domain processing algorithms, in order to create well focused SAR imagery suitable for interferometry. The time-domain backprojection algorithm inherently compensates for non-linear platform motion, dependent on the availability of accurate measurements of the motion. The implementation must manage the relatively high computational burden of the backprojection algorithm, which is done using modern graphics processing units (GPUs), programmed with NVIDIA’s CUDA language. An implementation of the Non-Equispaced Fast Fourier Transform (NERFFT) is used to enable efficient and accurate range interpolation as a critical step of the processing. The phase of time- domain processed imagery is dif erent than that of frequency-domain imagery, leading to a potentially different approach to interferometry. This general purpose SAR processor is designed to work with a novel, dual-frequency S- and Ka-band radar system developed at MIRSL as well as the UAVSAR instrument developed by NASA’s Jet Propulsion Laboratory. These instruments represent a wide range of SAR system parameters, ensuring the ability of the processor to work with most any airborne SAR. Results are presented from these two systems, showing good performance of the processor itself
CHARMM: The biomolecular simulation program
CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and widely used molecular simulation program. It has been developed over the last three decades with a primary focus on molecules of biological interest, including proteins, peptides, lipids, nucleic acids, carbohydrates, and small molecule ligands, as they occur in solution, crystals, and membrane environments. For the study of such systems, the program provides a large suite of computational tools that include numerous conformational and path sampling methods, free energy estimators, molecular minimization, dynamics, and analysis techniques, and model-building capabilities. The CHARMM program is applicable to problems involving a much broader class of many-particle systems. Calculations with CHARMM can be performed using a number of different energy functions and models, from mixed quantum mechanical-molecular mechanical force fields, to all-atom classical potential energy functions with explicit solvent and various boundary conditions, to implicit solvent and membrane models. The program has been ported to numerous platforms in both serial and parallel architectures. This article provides an overview of the program as it exists today with an emphasis on developments since the publication of the original CHARMM article in 1983. © 2009 Wiley Periodicals, Inc.J Comput Chem, 2009.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63074/1/21287_ftp.pd
Virtual Reality Games for Motor Rehabilitation
This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion
The Third Gravitational Lensing Accuracy Testing (GREAT3) Challenge Handbook
The GRavitational lEnsing Accuracy Testing 3 (GREAT3) challenge is the third in a series of image analysis challenges, with a goal of testing and facilitating the development of methods for analyzing astronomical images that will be used to measure weak gravitational lensing. This measurement requires extremely precise estimation of very small galaxy shape distortions, in the presence of far larger intrinsic galaxy shapes and distortions due to the blurring kernel caused by the atmosphere, telescope optics, and instrumental effects. The GREAT3 challenge is posed to the astronomy, machine learning, and statistics communities, and includes tests of three specific effects that are of immediate relevance to upcoming weak lensing surveys, two of which have never been tested in a community challenge before. These effects include many novel aspects including realistically complex galaxy models based on high-resolution imaging from space; a spatially varying, physically motivated blurring kernel; and a combination of multiple different exposures. To facilitate entry by people new to the field, and for use as a diagnostic tool, the simulation software for the challenge is publicly available, though the exact parameters used for the challenge are blinded. Sample scripts to analyze the challenge data using existing methods will also be provided. See http://great3challenge.info and http://great3.projects.phys.ucl.ac.uk/leaderboard/ for more information
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