Image registration is a well-defined computation paradigm widely applied to align one or more images to a target image. This paradigm, which builds upon three main components, is particularly compute-intensive and represents many image processing pipelines' bottlenecks. State-of-the-art solutions leverage hardware acceleration to speed up image registration, but they are usually limited to implementing a single component. We present Faber, an open-source HW/SW CAD toolchain tailored to image registration. The Faber toolchain comprises HW/SW highly-tunable registration components, supports users with different expertise in building custom pipelines, and automates the design process. In this direction, Faber provides both default settings for entry-level users and latency and resource models to guide HW experts in customizing the different components. Finally, Faber achieves from 1.5x to 54x in speedup and from 2x to 177x in energy efficiency against state-of-the-art tools on a Xeon Gold.

Faber: a Hardware/Software Toolchain for Image Registration

D'Arnese, Eleonora;Conficconi, Davide;Sozzo, Emanuele Del;Sciuto, Donatella;Santambrogio, Marco D.
2023-01-01

Abstract

Image registration is a well-defined computation paradigm widely applied to align one or more images to a target image. This paradigm, which builds upon three main components, is particularly compute-intensive and represents many image processing pipelines' bottlenecks. State-of-the-art solutions leverage hardware acceleration to speed up image registration, but they are usually limited to implementing a single component. We present Faber, an open-source HW/SW CAD toolchain tailored to image registration. The Faber toolchain comprises HW/SW highly-tunable registration components, supports users with different expertise in building custom pipelines, and automates the design process. In this direction, Faber provides both default settings for entry-level users and latency and resource models to guide HW experts in customizing the different components. Finally, Faber achieves from 1.5x to 54x in speedup and from 2x to 177x in energy efficiency against state-of-the-art tools on a Xeon Gold.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1223163
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