179,016 research outputs found
Computation of Trusted Short Weierstrass Elliptic Curves for Cryptography
Short Weierstrass's elliptic curves with underlying hard Elliptic Curve
Discrete Logarithm Problems was widely used in Cryptographic applications. This
paper introduces a new security notation 'trusted security' for computation
methods of elliptic curves for cryptography. Three additional "trusted security
acceptance criteria" is proposed to be met by the elliptic curves aimed for
cryptography. Further, two cryptographically secure elliptic curves over 256
bit and 384 bit prime fields are demonstrated which are secure from ECDLP, ECC
as well as trust perspectives. The proposed elliptic curves are successfully
subjected to thorough security analysis and performance evaluation with respect
to key generation and signing/verification and hence, proven for their
cryptographic suitability and great feasibility for acceptance by the
community.Comment: CYBERNETICS AND INFORMATION TECHNOLOGIES, Volume 21, No
Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices
Convolutional Neural Networks (CNNs) have revolutionized the research in
computer vision, due to their ability to capture complex patterns, resulting in
high inference accuracies. However, the increasingly complex nature of these
neural networks means that they are particularly suited for server computers
with powerful GPUs. We envision that deep learning applications will be
eventually and widely deployed on mobile devices, e.g., smartphones,
self-driving cars, and drones. Therefore, in this paper, we aim to understand
the resource requirements (time, memory) of CNNs on mobile devices. First, by
deploying several popular CNNs on mobile CPUs and GPUs, we measure and analyze
the performance and resource usage for every layer of the CNNs. Our findings
point out the potential ways of optimizing the performance on mobile devices.
Second, we model the resource requirements of the different CNN computations.
Finally, based on the measurement, pro ling, and modeling, we build and
evaluate our modeling tool, Augur, which takes a CNN configuration (descriptor)
as the input and estimates the compute time and resource usage of the CNN, to
give insights about whether and how e ciently a CNN can be run on a given
mobile platform. In doing so Augur tackles several challenges: (i) how to
overcome pro ling and measurement overhead; (ii) how to capture the variance in
different mobile platforms with different processors, memory, and cache sizes;
and (iii) how to account for the variance in the number, type and size of
layers of the different CNN configurations
The DUNE-ALUGrid Module
In this paper we present the new DUNE-ALUGrid module. This module contains a
major overhaul of the sources from the ALUgrid library and the binding to the
DUNE software framework. The main changes include user defined load balancing,
parallel grid construction, and an redesign of the 2d grid which can now also
be used for parallel computations. In addition many improvements have been
introduced into the code to increase the parallel efficiency and to decrease
the memory footprint.
The original ALUGrid library is widely used within the DUNE community due to
its good parallel performance for problems requiring local adaptivity and
dynamic load balancing. Therefore, this new model will benefit a number of DUNE
users. In addition we have added features to increase the range of problems for
which the grid manager can be used, for example, introducing a 3d tetrahedral
grid using a parallel newest vertex bisection algorithm for conforming grid
refinement. In this paper we will discuss the new features, extensions to the
DUNE interface, and explain for various examples how the code is used in
parallel environments.Comment: 25 pages, 11 figure
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