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Implementation and Scalability Evaluation of Random Forests for In-Switch Inference

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Published-A1-Poster (919.0Kb)
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URI: https://hdl.handle.net/20.500.12761/1692
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Autor(es)
Akem, Aristide Tanyi-Jong; Gucciardo, Michele; Fiore, Marco
Fecha
2022-06-08
Resumen
We comparatively evaluate state-of-the-art solutions for in-switch machine learning inference. We demonstrate that random forest (RF) models attain accuracies on par with those of approaches based on neural networks, which are also less amenable to in-switch operation. Next, we implement the top two solutions for in-switch random forest representation using a unified framework that we propose to ensure their fair comparison. We then verify their performance, resource consumption and scalability with respect to the capabilities of production-grade programmable switches.
Compartir
Ficheros
Published-A1-Poster (919.0Kb)
Identificadores
URI: https://hdl.handle.net/20.500.12761/1692
Metadatos
Mostrar el registro completo del ítem

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