An adaptive Sequential Monte Carlo framework with runtime HW/SW repartitioning

The considerable computational complexity of sequential 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 control-ce...

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Bibliographic Details
Published in:2009 International Conference on Field-Programmable Technology pp. 175 - 182
Main Authors: Happe, Markus, Lubbers, Enno, Platzner, Marco
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2009
Subjects:
Computational complexity
Field programmable gate arrays
Hardware
Monte Carlo methods
Operating systems
Particle filters
Particle tracking
Runtime
Sliding mode control
State estimation
ISBN:9781424443758, 142444375X
Online Access:Get full text
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Summary:The considerable computational complexity of sequential 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 control-centric sequential as well as the data-parallel parts of an SMC application. Determining the optimal HW/SW partitioning is challenging in general, and since in most cases the optimal partitioning is data-dependent even impossible with a design time approach. In this paper, we present a framework for implementing SMC methods on CPU/FPGA based systems such as modern platform FPGAs. Based on a multithreaded programming model, our framework allows for an easy design space exploration with respect to the HW/SW partitioning. Additionally, an SMC application can adaptively switch between several partitionings during run-time to react to changing input data and performance requirements. To show its feasibility and evaluate its performance and area requirements, we demonstrate the framework on two real-world case studies and show that partial reconfiguration can be effectively and transparently used for realizing adaptive HW/SW systems.
ISBN:9781424443758
142444375X
DOI:10.1109/FPT.2009.5377645