Approche haut niveau pour l’accélération d’algorithmes sur des architectures hétérogènes CPU/GPU/FPGA. Application à la qualification des radars et des systèmes d’écoute électromagnétique

Abstract : As the semiconductor industry faces major challenges in sustaining its growth, new High-Level Synthesis tools are repositioning FPGAs as a leading technology for algorithm acceleration in the face of CPU and GPU-based clusters. But as it stands, for a software engineer, these tools do not guarantee, without expertise of the underlying hardware, that these technologies will be harnessed to their full potential. This can be a game breaker for their democratization. From this observation, we propose a methodology for algorithm acceleration on FPGAs. After presenting a high-level model of this architecture, we detail possible optimizations in OpenCL, and finally define a relevant exploration strategy for accelerating algorithms on FPGA. Applied to different case studies, from tomographic reconstruction to the modelling of an airborne radar jammer, we evaluate our methodology according to three main performance criteria: development time, execution time, and energy efficiency.
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Maxime Martelli. Approche haut niveau pour l’accélération d’algorithmes sur des architectures hétérogènes CPU/GPU/FPGA. Application à la qualification des radars et des systèmes d’écoute électromagnétique. Calcul parallèle, distribué et partagé [cs.DC]. Université Paris-Saclay, 2019. Français. ⟨NNT : 2019SACLS581⟩. ⟨tel-02446848⟩

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