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Jeffrey Kingyens, J. Gregory Steffan
We propose a soft processor programming model and architecture inspired by graphics processing units (GPUs) that are well-matched to the strengths of FPGAs, namely, highly parallel and pipelinable computation. In particular, our soft processor architecture exploits multithreading, vector operations, and predication to supply a floating-point pipeline of 64 stages via hardware support for up to […]
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Jeffrey Richard Code Kingyens
In this thesis a soft processor programming model and architecture is proposed that is inspired by graphics processing units (GPUs) and well-matched to the strengths of FPGAs, namely highly-parallel and pipelinable computation. The proposed soft processor architecture exploits multithreading, vector operations, and predication to supply a floating-point pipeline of up to 60 stages via hardware […]
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Jeffrey Richard Code Kingyens, J. Gregory Steffan
There is building interest in using FPGAs as accelerators for high-performance computing, but existing systems for programming them are so far inadequate. In this paper we propose a soft processor programming model and architecture inspired by graphics processing units (GPUs) that are well-matched to the strengths of FPGAs, namely highly-parallel and pipelinable computation. In particular, […]
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Radomir Mech, Przemyslaw Prusinkiewicz
The introduction of floating-point pixel shaders has initiated a trend of moving algorithms from CPUs to graphics cards. The first algorithms were in the rendering domain, but recently we have witnessed increased interest in modeling algorithms as well. In this paper we present techniques for generating subdivision curves on a modern Graphics Processing Unit (GPU). […]
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