110,785 research outputs found
PowerPlanningDL: Reliability-Aware Framework for On-Chip Power Grid Design using Deep Learning
With the increase in the complexity of chip designs, VLSI physical design has
become a time-consuming task, which is an iterative design process. Power
planning is that part of the floorplanning in VLSI physical design where power
grid networks are designed in order to provide adequate power to all the
underlying functional blocks. Power planning also requires multiple iterative
steps to create the power grid network while satisfying the allowed worst-case
IR drop and Electromigration (EM) margin. For the first time, this paper
introduces Deep learning (DL)-based framework to approximately predict the
initial design of the power grid network, considering different reliability
constraints. The proposed framework reduces many iterative design steps and
speeds up the total design cycle. Neural Network-based multi-target regression
technique is used to create the DL model. Feature extraction is done, and the
training dataset is generated from the floorplans of some of the power grid
designs extracted from the IBM processor. The DL model is trained using the
generated dataset. The proposed DL-based framework is validated using a new set
of power grid specifications (obtained by perturbing the designs used in the
training phase). The results show that the predicted power grid design is
closer to the original design with minimal prediction error (~2%). The proposed
DL-based approach also improves the design cycle time with a speedup of ~6X for
standard power grid benchmarks.Comment: Published in proceedings of IEEE/ACM Design, Automation and Test in
Europe Conference (DATE) 2020, 6 page
Sub-grid modelling for two-dimensional turbulence using neural networks
In this investigation, a data-driven turbulence closure framework is
introduced and deployed for the sub-grid modelling of Kraichnan turbulence. The
novelty of the proposed method lies in the fact that snapshots from
high-fidelity numerical data are used to inform artificial neural networks for
predicting the turbulence source term through localized grid-resolved
information. In particular, our proposed methodology successfully establishes a
map between inputs given by stencils of the vorticity and the streamfunction
along with information from two well-known eddy-viscosity kernels. Through this
we predict the sub-grid vorticity forcing in a temporally and spatially dynamic
fashion. Our study is both a-priori and a-posteriori in nature. In the former,
we present an extensive hyper-parameter optimization analysis in addition to
learning quantification through probability density function based validation
of sub-grid predictions. In the latter, we analyse the performance of our
framework for flow evolution in a classical decaying two-dimensional turbulence
test case in the presence of errors related to temporal and spatial
discretization. Statistical assessments in the form of angle-averaged kinetic
energy spectra demonstrate the promise of the proposed methodology for sub-grid
quantity inference. In addition, it is also observed that some measure of
a-posteriori error must be considered during optimal model selection for
greater accuracy. The results in this article thus represent a promising
development in the formalization of a framework for generation of
heuristic-free turbulence closures from data
Verification of Neural Network Behaviour: Formal Guarantees for Power System Applications
This paper presents for the first time, to our knowledge, a framework for
verifying neural network behavior in power system applications. Up to this
moment, neural networks have been applied in power systems as a black-box; this
has presented a major barrier for their adoption in practice. Developing a
rigorous framework based on mixed integer linear programming, our methods can
determine the range of inputs that neural networks classify as safe or unsafe,
and are able to systematically identify adversarial examples. Such methods have
the potential to build the missing trust of power system operators on neural
networks, and unlock a series of new applications in power systems. This paper
presents the framework, methods to assess and improve neural network robustness
in power systems, and addresses concerns related to scalability and accuracy.
We demonstrate our methods on the IEEE 9-bus, 14-bus, and 162-bus systems,
treating both N-1 security and small-signal stability.Comment: published in IEEE Transactions on Smart Grid
(https://ieeexplore.ieee.org/abstract/document/9141308
A scalable system for microcalcification cluster automated detection in a distributed mammographic database
A computer-aided detection (CADe) system for microcalcification cluster
identification in mammograms has been developed in the framework of the
EU-founded MammoGrid project. The CADe software is mainly based on wavelet
transforms and artificial neural networks. It is able to identify
microcalcifications in different datasets of mammograms (i.e. acquired with
different machines and settings, digitized with different pitch and bit depth
or direct digital ones). The CADe can be remotely run from GRID-connected
acquisition and annotation stations, supporting clinicians from geographically
distant locations in the interpretation of mammographic data. We report and
discuss the system performances on different datasets of mammograms and the
status of the GRID-enabled CADe analysis.Comment: 6 pages, 4 figures; Proceedings of the IEEE NNS and MIC Conference,
October 23-29, 2005, Puerto Ric
A neural ordinary differential equations based approach for demand forecasting within power grid digital twins
Over the past few years, deep learning (DL) based electricity demand forecasting has received considerable attention amongst mathematicians, engineers and data scientists working within the smart grid domain. To this end, deep learning architectures such as deep neural networks (DNN), deep belief networks (DBN) and recurrent neural networks (RNN) have been successfully applied to forecast the generation and consumption of a wide range of energy vectors. In this work, we show preliminary results for a residential load demand forecasting solution which is realized within the framework of power grid digital twin. To this end, a novel class of deep neural networks is adopted wherein the output of the network is efficiently computed via a black-box ordinary differential equation (ODE) solver. We introduce the readers to the main concepts behind this method followed by a real-world, data driven computational benchmark test case designed to study the numerical effectiveness of the proposed approach. Initial results suggest that the ODE based solutions yield acceptable levels of accuracy for wide range of prediction horizons. We conclude that the method could prove as a valuable tool to develop forecasting models within an electrical digital twin (EDT) framework, where, in addition to accurate prediction models, a time horizon independent, computationally scalable and compact model is often desired.This research that contributed to this paper was funded by the EPSRC/Innovate UK Centre for Smart Infrastructure and
Construction (CSIC) and Centre for Digital Built Britain (CDBB) at the University of Cambridge
GRINN: A Physics-Informed Neural Network for solving hydrodynamic systems in the presence of self-gravity
Modeling self-gravitating gas flows is essential to answering many
fundamental questions in astrophysics. This spans many topics including
planet-forming disks, star-forming clouds, galaxy formation, and the
development of large-scale structures in the Universe. However, the nonlinear
interaction between gravity and fluid dynamics offers a formidable challenge to
solving the resulting time-dependent partial differential equations (PDEs) in
three dimensions (3D). By leveraging the universal approximation capabilities
of a neural network within a mesh-free framework, physics informed neural
networks (PINNs) offer a new way of addressing this challenge. We introduce the
gravity-informed neural network (GRINN), a PINN-based code, to simulate 3D
self-gravitating hydrodynamic systems. Here, we specifically study
gravitational instability and wave propagation in an isothermal gas. Our
results match a linear analytic solution to within 1\% in the linear regime and
a conventional grid code solution to within 5\% as the disturbance grows into
the nonlinear regime. We find that the computation time of the GRINN does not
scale with the number of dimensions. This is in contrast to the scaling of the
grid-based code for the hydrodynamic and self-gravity calculations as the
number of dimensions is increased. Our results show that the GRINN computation
time is longer than the grid code in one- and two- dimensional calculations but
is an order of magnitude lesser than the grid code in 3D with similar accuracy.
Physics-informed neural networks like GRINN thus show promise for advancing our
ability to model 3D astrophysical flows
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