11,980 research outputs found
An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration
We empirically evaluate an undervolting technique, i.e., underscaling the
circuit supply voltage below the nominal level, to improve the power-efficiency
of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable
Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing
faults due to excessive circuit latency increase. We evaluate the
reliability-power trade-off for such accelerators. Specifically, we
experimentally study the reduced-voltage operation of multiple components of
real FPGAs, characterize the corresponding reliability behavior of CNN
accelerators, propose techniques to minimize the drawbacks of reduced-voltage
operation, and combine undervolting with architectural CNN optimization
techniques, i.e., quantization and pruning. We investigate the effect of
environmental temperature on the reliability-power trade-off of such
accelerators. We perform experiments on three identical samples of modern
Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification
CNN benchmarks. This approach allows us to study the effects of our
undervolting technique for both software and hardware variability. We achieve
more than 3X power-efficiency (GOPs/W) gain via undervolting. 2.6X of this gain
is the result of eliminating the voltage guardband region, i.e., the safe
voltage region below the nominal level that is set by FPGA vendor to ensure
correct functionality in worst-case environmental and circuit conditions. 43%
of the power-efficiency gain is due to further undervolting below the
guardband, which comes at the cost of accuracy loss in the CNN accelerator. We
evaluate an effective frequency underscaling technique that prevents this
accuracy loss, and find that it reduces the power-efficiency gain from 43% to
25%.Comment: To appear at the DSN 2020 conferenc
Applying model-based systems engineering to architecture optimization and selection during system acquisition
2018 Fall.Includes bibliographical references.The architecture selection process early in a major system acquisition is a critical step in determining the overall affordability and technical performance success of a program. There are recognized deficiencies that frequently occur in this step such as poor transparency into the final selection decision and excessive focus on lowest cost, which is not necessarily the best value for all of the stakeholders. This research investigates improvements to the architecture selection process by integrating Model-Based Systems Engineering (MBSE) techniques, enforcing rigorous, quantitative evaluation metrics with a corresponding understanding of uncertainties, and stakeholder feedback in order to generate an architecture that is more optimized and trusted to provide better value for the stakeholders. Three case studies were analyzed to demonstrate this proposed process. The first focused on a satellite communications System of Systems (SoS) acquisition to demonstrate the overall feasibility and applicability of the process. The second investigated an electro-optical remote sensing satellite system to compare this proposed process to a current architecture selection process typified by the United States Department of Defense (U.S. DoD) Analysis of Alternatives (AoA). The third case study analyzed the evaluation of a service-oriented architecture (SOA) providing satellite command and control with cyber security protections in order to demonstrate rigorous accounting of uncertainty through the architecture evaluation and selection. These case studies serve to define and demonstrate a new, more transparent and trusted architecture selection process that consistently provides better value for the stakeholders of a major system acquisition. While the examples in this research focused on U.S. DoD and other major acquisitions, the methodology developed is broadly applicable to other domains where this is a need for optimization of enterprise architectures as the basis for effective system acquisition. The results from the three case studies showed the new process outperformed the current methodology for conducting architecture evaluations in nearly all criteria considered and in particular selects architectures of better value, provides greater visibility into the actual decision making, and improves trust in the decision through a robust understanding of uncertainty. The primary contribution of this research then is improved information support to an architecture selection in the early phases of a system acquisition program. The proposed methodology presents a decision authority with an integrated assessment of each alternative, traceable to the concerns of the system's stakeholders, and thus enables a more informed and objective selection of the preferred alternative. It is recommended that the methodology proposed in this work is considered for future architecture evaluations
Communication of Simulation and Modelling Activities in Early Systems Engineering
In this paper we present a framework that aids and supports communication of modeling and simulation activities in early systems engineering. We do this by analyzing existing simulation and modelling frameworks, both in systems engineering as well as more generic frameworks. For each framework, we discuss its purpose, main outcomes and the tools and methods used in the framework. Using this overview, we argue that in order to apply simulation and modeling techniques fully in conceptual systems design, it is necessary to use a framework focused on communication and aimed at four key issues. We extract a generic process from the discussed frameworks and discuss for each step of this process how these issues should be addressed. We also explain how this framework should be supported with tooling. Finally we discuss a simulation study of a medical imaging system that gave us initial experiences on the approach presented here. We conclude that this framework shows promise in supporting the communication of a modeling and simulation study in a multidisciplinary settin
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
This report documents the program and the outcomes of GI-Dagstuhl Seminar
16394 "Software Performance Engineering in the DevOps World".
The seminar addressed the problem of performance-aware DevOps. Both, DevOps
and performance engineering have been growing trends over the past one to two
years, in no small part due to the rise in importance of identifying
performance anomalies in the operations (Ops) of cloud and big data systems and
feeding these back to the development (Dev). However, so far, the research
community has treated software engineering, performance engineering, and cloud
computing mostly as individual research areas. We aimed to identify
cross-community collaboration, and to set the path for long-lasting
collaborations towards performance-aware DevOps.
The main goal of the seminar was to bring together young researchers (PhD
students in a later stage of their PhD, as well as PostDocs or Junior
Professors) in the areas of (i) software engineering, (ii) performance
engineering, and (iii) cloud computing and big data to present their current
research projects, to exchange experience and expertise, to discuss research
challenges, and to develop ideas for future collaborations
Enhancing Energy Production with Exascale HPC Methods
High Performance Computing (HPC) resources have become the key actor for achieving more ambitious challenges in many disciplines. In this step beyond, an explosion on the available parallelism and the use of special purpose
processors are crucial. With such a goal, the HPC4E project applies new exascale HPC techniques to energy industry simulations, customizing them if necessary, and going beyond the state-of-the-art in the required HPC exascale
simulations for different energy sources. In this paper, a general overview of these methods is presented as well as some specific preliminary results.The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) under the HPC4E Project (www.hpc4e.eu), grant agreement n° 689772, the Spanish Ministry of
Economy and Competitiveness under the CODEC2 project (TIN2015-63562-R), and
from the Brazilian Ministry of Science, Technology and Innovation through Rede
Nacional de Pesquisa (RNP). Computer time on Endeavour cluster is provided by the
Intel Corporation, which enabled us to obtain the presented experimental results in
uncertainty quantification in seismic imagingPostprint (author's final draft
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