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An Adaptive Sequential Monte Carlo Framework with Runtime HW/SW Repartitioning
"... Abstract—The considerable computational complexity of Se-quential Monte Carlo (SMC) methods is a major obstacle when implementing them on CPU-based resource constrained embedded systems. Hybrid CPU/FPGA systems, on the other hand, are a more suitable target, as they can efficiently execute both the ..."
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Abstract—The considerable computational complexity of Se-quential Monte Carlo (SMC) methods is a major obstacle when implementing them on CPU-based resource constrained embedded systems. Hybrid CPU/FPGA systems, on the other hand, are a more suitable target, as they can efficiently execute both
Architectural Exploration of Reconfigurable Monte-Carlo Simulations using a High-Level Synthesis Approach
"... This paper describes an approach for automatically generating programs that can be compiled into efficient hardware architectures, and illustrates its application in producing designs for Monte-Carlo simulation that are optimised for user requirements in resource utilisation or execution time. Monte ..."
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This paper describes an approach for automatically generating programs that can be compiled into efficient hardware architectures, and illustrates its application in producing designs for Monte-Carlo simulation that are optimised for user requirements in resource utilisation or execution time
General Sequential Sampling Techniques for Monte Carlo Simulations: Part I - Matrix Problems
, 1996
"... We study sequential sampling methods based principally on ideas of Halton. Such methods are designed to build information drawn from early batches of random walk histories into the random walk process used to generate later histories in order to accelerate convergence. In previously published wo ..."
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We study sequential sampling methods based principally on ideas of Halton. Such methods are designed to build information drawn from early batches of random walk histories into the random walk process used to generate later histories in order to accelerate convergence. In previously published
Multi-level Customisation Framework for Curve Based Monte Carlo Financial Simulations
"... Abstract. One of the main challenges when accelerating financial applications using reconfigurable hardware is the management of design complexity. This paper proposes a multi-level customisation framework for automatic generation of complex yet highly efficient curve based financial Monte Carlo sim ..."
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Abstract. One of the main challenges when accelerating financial applications using reconfigurable hardware is the management of design complexity. This paper proposes a multi-level customisation framework for automatic generation of complex yet highly efficient curve based financial Monte Carlo
A hardware Gaussian noise generator using the Wallace method
- IEEE Transactions on VLSI
, 2005
"... Abstract—We describe a hardware Gaussian noise generator based on the Wallace method used for a hardware simulation system. Our noise generator accurately models a true Gaussian probability density function even at high values. We evaluate its properties using: 1) several different statistical tests ..."
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Cited by 23 (9 self)
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of the noise generator on an XC2V4000-6 FPGA at 115 MHz can run 51 times faster than software on a 2.6-GHz Pentium-IV PC. Index Terms—Channel coding, communication channels, field-programmable gate arrays (FPGAs), Gaussian noise, highperformance, Monte Carlo methods, reconfigurable-computing, technology-mapping.
¶Altera Europe Limited
"... Abstract—The Sequential Monte Carlo (SMC) method is a simulation-based approach to compute posterior distributions. SMC methods often work well on applications considered intractable by other methods due to high dimensionality, but they are computationally demanding. While SMC has been implemented e ..."
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Abstract—The Sequential Monte Carlo (SMC) method is a simulation-based approach to compute posterior distributions. SMC methods often work well on applications considered intractable by other methods due to high dimensionality, but they are computationally demanding. While SMC has been implemented
Non-Gaussian Bridge Sampling with an Application∗
"... This paper provides a new bridge sampler that can efficiently generate sample paths, subject to some endpoint condition, for non-Gaussian dynamic models. This bridge sampler uses a companion pseudo-Gaussian bridge as the proposal and sequentially re-simulates sample paths via a sequence of tempered ..."
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importance weights in a way bearing resemblance to the density-tempered sequential Monte Carlo method used in the Bayesian statistics literature. This bridge sampler is further accelerated by employing a novel idea of k-fold duplicating a base set of sample paths followed by support boosting. We implement
A Flexible Arithmetic System for Simulation
, 2007
"... person(s) intending to use a part or the whole of the materials in this thesis in a proposed publication must seek copyright release from the Dean of the Graduate School. Custom hardware accelerators are commonly used in simulation systems requiring high computational power. Such applications often ..."
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to quickly explore the design space in three dimensions: the number system, the operator architecture and the configuration of individual opera-tors. It utilizes sophisticated arithmetic algorithms and reconfigurable architectures, captured in the object libraries. The final result is an optimized datapath
unknown title
"... Anne SABOURIN Mélanges bayésiens de modèles d'extrêmes multivariés, Application à la prédétermination régionale des crues avec données incomplètes. Sous la direction de: ..."
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Anne SABOURIN Mélanges bayésiens de modèles d'extrêmes multivariés, Application à la prédétermination régionale des crues avec données incomplètes. Sous la direction de:
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