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Preparing sparse solvers for exascale computing.
Sparse solvers provide essential functionality for a wide variety of scientific applications. Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi-physics and multi-scale simulations, especially as we target exascale platforms. This paper describes the challenges, strategies and progress of the US Department of Energy Exascale Computing project towards providing sparse solvers for exascale computing platforms. We address the demands of systems with thousands of high-performance node devices where exposing concurrency, hiding latency and creating alternative algorithms become essential. The efforts described here are works in progress, highlighting current success and upcoming challenges. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'
Massively parallel approximate Gaussian process regression
We explore how the big-three computing paradigms -- symmetric multi-processor
(SMC), graphical processing units (GPUs), and cluster computing -- can together
be brought to bare on large-data Gaussian processes (GP) regression problems
via a careful implementation of a newly developed local approximation scheme.
Our methodological contribution focuses primarily on GPU computation, as this
requires the most care and also provides the largest performance boost.
However, in our empirical work we study the relative merits of all three
paradigms to determine how best to combine them. The paper concludes with two
case studies. One is a real data fluid-dynamics computer experiment which
benefits from the local nature of our approximation; the second is a synthetic
data example designed to find the largest design for which (accurate) GP
emulation can performed on a commensurate predictive set under an hour.Comment: 24 pages, 6 figures, 1 tabl
U.S. Corporate Leverage: Developments in 1987 and 1988
macroeconomics, U.S. Corporate Leverage, Developments, 1987, 1988
Autonomously Reconfigurable Artificial Neural Network on a Chip
Artificial neural network (ANN), an established bio-inspired computing paradigm, has proved very effective in a variety of real-world problems and particularly useful for various emerging biomedical applications using specialized ANN hardware. Unfortunately, these ANN-based systems are increasingly vulnerable to both transient and permanent faults due to unrelenting advances in CMOS technology scaling, which sometimes can be catastrophic. The considerable resource and energy consumption and the lack of dynamic adaptability make conventional fault-tolerant techniques unsuitable for future portable medical solutions. Inspired by the self-healing and self-recovery mechanisms of human nervous system, this research seeks to address reliability issues of ANN-based hardware by proposing an Autonomously Reconfigurable Artificial Neural Network (ARANN) architectural framework. Leveraging the homogeneous structural characteristics of neural networks, ARANN is capable of adapting its structures and operations, both algorithmically and microarchitecturally, to react to unexpected neuron failures. Specifically, we propose three key techniques --- Distributed ANN, Decoupled Virtual-to-Physical Neuron Mapping, and Dual-Layer Synchronization --- to achieve cost-effective structural adaptation and ensure accurate system recovery. Moreover, an ARANN-enabled self-optimizing workflow is presented to adaptively explore a "Pareto-optimal" neural network structure for a given application, on the fly. Implemented and demonstrated on a Virtex-5 FPGA, ARANN can cover and adapt 93% chip area (neurons) with less than 1% chip overhead and O(n) reconfiguration latency. A detailed performance analysis has been completed based on various recovery scenarios
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
Embedded Machine Learning: Emphasis on Hardware Accelerators and Approximate Computing for Tactile Data Processing
Machine Learning (ML) a subset of Artificial Intelligence (AI) is driving the industrial
and technological revolution of the present and future. We envision a world with smart
devices that are able to mimic human behavior (sense, process, and act) and perform
tasks that at one time we thought could only be carried out by humans. The vision
is to achieve such a level of intelligence with affordable, power-efficient, and fast hardware
platforms. However, embedding machine learning algorithms in many application domains
such as the internet of things (IoT), prostheses, robotics, and wearable devices is an ongoing
challenge. A challenge that is controlled by the computational complexity of ML algorithms,
the performance/availability of hardware platforms, and the application\u2019s budget (power
constraint, real-time operation, etc.). In this dissertation, we focus on the design and
implementation of efficient ML algorithms to handle the aforementioned challenges. First, we
apply Approximate Computing Techniques (ACTs) to reduce the computational complexity of
ML algorithms. Then, we design custom Hardware Accelerators to improve the performance
of the implementation within a specified budget. Finally, a tactile data processing application
is adopted for the validation of the proposed exact and approximate embedded machine
learning accelerators.
The dissertation starts with the introduction of the various ML algorithms used for
tactile data processing. These algorithms are assessed in terms of their computational
complexity and the available hardware platforms which could be used for implementation.
Afterward, a survey on the existing approximate computing techniques and hardware
accelerators design methodologies is presented. Based on the findings of the survey, an
approach for applying algorithmic-level ACTs on machine learning algorithms is provided.
Then three novel hardware accelerators are proposed: (1) k-Nearest Neighbor (kNN) based
on a selection-based sorter, (2) Tensorial Support Vector Machine (TSVM) based on Shallow
Neural Networks, and (3) Hybrid Precision Binary Convolution Neural Network (BCNN).
The three accelerators offer a real-time classification with monumental reductions in the
hardware resources and power consumption compared to existing implementations targeting
the same tactile data processing application on FPGA. Moreover, the approximate accelerators
maintain a high classification accuracy with a loss of at most 5%
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