9,823 research outputs found
Functional Verification of Digital Systems Using Meta-Heuristic Algorithms
Trends in technological developments, such as autonomous vehicles, home automation, connected cars, IoT, etc., are based on integrated systems or application-specific integrated circuits with high capacities, where these systems require even more complex devices. Thus, new techniques to design more secure systems in a short time in the market are needed. At this point, verification is one of the highest costs in the manufacturing stage and most expensive in the design process. To reduce the time and cost of the verification process, artificial intelligence techniques based on the optimization of the coverage of behavioral areas have been proposed. In this chapter, we will describe the main techniques used in the functional verification of digital systems of medium complexity, focusing especially on meta-heuristic algorithms such as particle swarm optimization, genetic algorithms, and so on. Several results are presented and compared, where the opportunity areas will be described
Open-ended evolution to discover analogue circuits for beyond conventional applications
This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s10710-012-9163-8. Copyright @ Springer 2012.Analogue circuits synthesised by means of open-ended evolutionary algorithms often have unconventional designs. However, these circuits are typically highly compact, and the general nature of the evolutionary search methodology allows such designs to be used in many applications. Previous work on the evolutionary design of analogue circuits has focused on circuits that lie well within analogue application domain. In contrast, our paper considers the evolution of analogue circuits that are usually synthesised in digital logic. We have developed four computational circuits, two voltage distributor circuits and a time interval metre circuit. The approach, despite its simplicity, succeeds over the design tasks owing to the employment of substructure reuse and incremental evolution. Our findings expand the range of applications that are considered suitable for evolutionary electronics
Paraiso : An Automated Tuning Framework for Explicit Solvers of Partial Differential Equations
We propose Paraiso, a domain specific language embedded in functional
programming language Haskell, for automated tuning of explicit solvers of
partial differential equations (PDEs) on GPUs as well as multicore CPUs. In
Paraiso, one can describe PDE solving algorithms succinctly using tensor
equations notation. Hydrodynamic properties, interpolation methods and other
building blocks are described in abstract, modular, re-usable and combinable
forms, which lets us generate versatile solvers from little set of Paraiso
source codes.
We demonstrate Paraiso by implementing a compressive hydrodynamics solver. A
single source code less than 500 lines can be used to generate solvers of
arbitrary dimensions, for both multicore CPUs and GPUs. We demonstrate both
manual annotation based tuning and evolutionary computing based automated
tuning of the program.Comment: 52 pages, 14 figures, accepted for publications in Computational
Science and Discover
Towards the Evolution of Novel Vertical-Axis Wind Turbines
Renewable and sustainable energy is one of the most important challenges
currently facing mankind. Wind has made an increasing contribution to the
world's energy supply mix, but still remains a long way from reaching its full
potential. In this paper, we investigate the use of artificial evolution to
design vertical-axis wind turbine prototypes that are physically instantiated
and evaluated under approximated wind tunnel conditions. An artificial neural
network is used as a surrogate model to assist learning and found to reduce the
number of fabrications required to reach a higher aerodynamic efficiency,
resulting in an important cost reduction. Unlike in other approaches, such as
computational fluid dynamics simulations, no mathematical formulations are used
and no model assumptions are made.Comment: 14 pages, 11 figure
Shingle 2.0: generalising self-consistent and automated domain discretisation for multi-scale geophysical models
The approaches taken to describe and develop spatial discretisations of the
domains required for geophysical simulation models are commonly ad hoc, model
or application specific and under-documented. This is particularly acute for
simulation models that are flexible in their use of multi-scale, anisotropic,
fully unstructured meshes where a relatively large number of heterogeneous
parameters are required to constrain their full description. As a consequence,
it can be difficult to reproduce simulations, ensure a provenance in model data
handling and initialisation, and a challenge to conduct model intercomparisons
rigorously. This paper takes a novel approach to spatial discretisation,
considering it much like a numerical simulation model problem of its own. It
introduces a generalised, extensible, self-documenting approach to carefully
describe, and necessarily fully, the constraints over the heterogeneous
parameter space that determine how a domain is spatially discretised. This
additionally provides a method to accurately record these constraints, using
high-level natural language based abstractions, that enables full accounts of
provenance, sharing and distribution. Together with this description, a
generalised consistent approach to unstructured mesh generation for geophysical
models is developed, that is automated, robust and repeatable, quick-to-draft,
rigorously verified and consistent to the source data throughout. This
interprets the description above to execute a self-consistent spatial
discretisation process, which is automatically validated to expected discrete
characteristics and metrics.Comment: 18 pages, 10 figures, 1 table. Submitted for publication and under
revie
Deep Space Network information system architecture study
The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control
Self-Scaling Evolution of Analog Computation Circuits
Energy and performance improvements of continuous-time analog-based computation for selected applications offer an avenue to continue improving the computational ability of tomorrow*s electronic devices at current technology scaling limits. However, analog computation is plagued by the difficulty of designing complex computational circuits, programmability, as well as the inherent lack of accuracy and precision when compared to digital implementations. In this thesis, evolutionary algorithm-based techniques are utilized within a reconfigurable analog fabric to realize an automated method of designing analog-based computational circuits while adapting the functional range to improve performance. A Self-Scaling Genetic Algorithm is proposed to adapt solutions to computationally-tractable ranges in hardware-constrained analog reconfigurable fabrics. It operates by utilizing a Particle Swarm Optimization (PSO) algorithm that operates synergistically with a Genetic Algorithm (GA) to adaptively scale and translate the functional range of computational circuits composed of high-level or low-level Computational Analog Elements to improve performance and realize functionality otherwise unobtainable on the intrinsic platform. The technique is demonstrated by evolving square, square-root, cube, and cube-root analog computational circuits on the Cypress PSoC-5LP System-on-Chip. Results indicate that the Self-Scaling Genetic Algorithm improves our error metric on average 7.18-fold, up to 12.92-fold for computational circuits that produce outputs beyond device range. Results were also favorable compared to previous works, which utilized extrinsic evolution of circuits with much greater complexity than was possible on the PSoC-5LP
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