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11 Typical FPGAs Data flow  

11 Typical FPGAs Data flow  

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Conference Paper
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This paper presents PARPIV the design and prototyping of a highly parameterized digital Particle Image Velocimetry (PIV) system implemented on reconfigurable hardware. Despite many improvements to PIV methods over the last twenty years, PIV post-processing remains a computationally intensive task. It becomes a serious bottleneck as camera acquisiti...

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Citations

... Results are shown for single kernel performance improvement, since KS is a kernel level optimization. The PIV kernels are also compared to previously optimized FPGA implementations [15], [16]. In addition, KS and RE kernel performance are compared. ...
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Graphics Processing Units (GPUs) are increasingly used to accelerate scientific applications. The state-of-the-art limits the adaptability of GPU kernels to both problem parameters and hardware characteristics. This makes writing high performance libraries for GPUs challenging. We address these challenges through Kernel Specialization (KS) which supports both user and hardware parameters and produces highly optimized GPU code. We apply KS to Particle Image Velocimetry (PIV), a technique used to obtain instantaneous velocity measurements in fluids for such diverse applications as aircraft design and artificial heart design. KS helps the user search PIV’s highly non-linear design space, supports a wide range of PIV parameters, and results in improved acceleration times over existing kernels.
... Beyond platform management, certain application areas require extensive parameterization to efficiently manage all operational alternatives within the source code. Bennis et al. [BeLT09] show a reconfigurable Particle Image Velocimetry (PIV) system to measure fluid velocities. The design is parameterized to adjust to application requirements both algorithmic properties (e.g., image size and window) and architecture (RAM bit width). ...
... Most of the approaches described in this chapter suffer from a missing general view. Either the solutions are tailored to a specific application [BeLT09,AEGH10,ScJA99,WaBL06], or their methodology is restricted to certain tools or programming resp. HW description languages [Xili06, Xili10g, Alte09, Acte10d, NeKo01]. ...
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This work describes the development of a model-free reinforcement learning-based control methodology for the heaving plate, a laboratory experimental fluid system that serves as a model of flapping flight. Through an optimized policy gradient algorithm, we were able to demonstrate rapid convergence (requiring less than 10 minutes of experiments) to a stroke form which maximized the propulsive efficiency of this very complicated fluid-dynamical system. This success was due in part to an improved sampling distribution and carefully selected policy parameterization, both motivated by a formal analysis of the signal-to-noise ratio of policy gradient algorithms. The resulting optimal policy provides insight into the behavior of the fluid system, and the effectiveness of the learning strategy suggests a number of exciting opportunities for machine learning control of fluid dynamics.