90,628 research outputs found
Deep Learning in the Automotive Industry: Applications and Tools
Deep Learning refers to a set of machine learning techniques that utilize
neural networks with many hidden layers for tasks, such as image
classification, speech recognition, language understanding. Deep learning has
been proven to be very effective in these domains and is pervasively used by
many Internet services. In this paper, we describe different automotive uses
cases for deep learning in particular in the domain of computer vision. We
surveys the current state-of-the-art in libraries, tools and infrastructures
(e.\,g.\ GPUs and clouds) for implementing, training and deploying deep neural
networks. We particularly focus on convolutional neural networks and computer
vision use cases, such as the visual inspection process in manufacturing plants
and the analysis of social media data. To train neural networks, curated and
labeled datasets are essential. In particular, both the availability and scope
of such datasets is typically very limited. A main contribution of this paper
is the creation of an automotive dataset, that allows us to learn and
automatically recognize different vehicle properties. We describe an end-to-end
deep learning application utilizing a mobile app for data collection and
process support, and an Amazon-based cloud backend for storage and training.
For training we evaluate the use of cloud and on-premises infrastructures
(including multiple GPUs) in conjunction with different neural network
architectures and frameworks. We assess both the training times as well as the
accuracy of the classifier. Finally, we demonstrate the effectiveness of the
trained classifier in a real world setting during manufacturing process.Comment: 10 page
Mapping the Gas Turbulence in the Coma Cluster: Predictions for Astro-H
Astro-H will be able for the first time to map gas velocities and detect
turbulence in galaxy clusters. One of the best targets for turbulence studies
is the Coma cluster, due to its proximity, absence of a cool core, and lack of
a central active galactic nucleus. To determine what constraints Astro-H will
be able to place on the Coma velocity field, we construct simulated maps of the
projected gas velocity and compute the second-order structure function, an
analog of the velocity power spectrum. We vary the injection scale, dissipation
scale, slope, and normalization of the turbulent power spectrum, and apply
measurement errors and finite sampling to the velocity field. We find that even
with sparse coverage of the cluster, Astro-H will be able to measure the Mach
number and the injection scale of the turbulent power spectrum--the quantities
determining the energy flux down the turbulent cascade and the diffusion rate
for everything that is advected by the gas (metals, cosmic rays, etc.). Astro-H
will not be sensitive to the dissipation scale or the slope of the power
spectrum in its inertial range, unless they are outside physically motivated
intervals. We give the expected confidence intervals for the injection scale
and the normalization of the power spectrum for a number of possible pointing
configurations, combining the structure function and velocity dispersion data.
Importantly, we also determine that measurement errors on the line shift will
bias the velocity structure function upward, and show how to correct this bias.Comment: 18 pages, 13 figures. Matches published ApJ version, except that it
fixes an error in the left panel of Figure 5 that is being addressed in an
ApJ erratu
Lessons Learned from a Decade of Providing Interactive, On-Demand High Performance Computing to Scientists and Engineers
For decades, the use of HPC systems was limited to those in the physical
sciences who had mastered their domain in conjunction with a deep understanding
of HPC architectures and algorithms. During these same decades, consumer
computing device advances produced tablets and smartphones that allow millions
of children to interactively develop and share code projects across the globe.
As the HPC community faces the challenges associated with guiding researchers
from disciplines using high productivity interactive tools to effective use of
HPC systems, it seems appropriate to revisit the assumptions surrounding the
necessary skills required for access to large computational systems. For over a
decade, MIT Lincoln Laboratory has been supporting interactive, on-demand high
performance computing by seamlessly integrating familiar high productivity
tools to provide users with an increased number of design turns, rapid
prototyping capability, and faster time to insight. In this paper, we discuss
the lessons learned while supporting interactive, on-demand high performance
computing from the perspectives of the users and the team supporting the users
and the system. Building on these lessons, we present an overview of current
needs and the technical solutions we are building to lower the barrier to entry
for new users from the humanities, social, and biological sciences.Comment: 15 pages, 3 figures, First Workshop on Interactive High Performance
Computing (WIHPC) 2018 held in conjunction with ISC High Performance 2018 in
Frankfurt, German
Performance analysis and optimization of automotive GPUs
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) have drastically increased the performance demands of automotive systems. Suitable highperformance platforms building upon Graphic Processing Units (GPUs) have been developed to respond to this demand, being NVIDIA Jetson TX2 a relevant representative. However, whether high-performance GPU configurations are appropriate for automotive setups remains as an open question. This paper aims at providing light on this question by modelling an automotive GPU (Jetson TX2), analyzing its microarchitectural parameters against relevant benchmarks, and identifying specific configurations able to meaningfully increase performance within similar cost envelopes, or to decrease costs preserving original performance levels. Overall, our analysis opens the door to the optimization of automotive GPUs for further system efficiency.This work has been partially supported by the Spanish
Ministry of Economy and Competitiveness (MINECO) under grant TIN2015-65316-P, the European Research Council
(ERC) under the European Union’s Horizon 2020 research
and innovation programme (grant agreement No. 772773) and
the HiPEAC Network of Excellence. Pedro Benedicte and
Jaume Abella have been partially supported by the MINECO
under FPU15/01394 grant and Ramon y Cajal postdoctoral fellowship number RYC-2013-14717 respectively and Leonidas
Kosmidis under Juan de la Cierva-Formacin postdoctoral fellowship (FJCI-2017-34095).Peer ReviewedPostprint (author's final draft
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