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Do switches dream of machine learning?

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If you have a question about this talk, please contact Marco Caballero.

Abstract: Machine learning is currently driving a technological and societal revolution. While programmable switches have been proven to be useful for in-network computing, machine learning within programmable switches had little success so far. Not using network devices for machine learning has a high toll, given the known power efficiency and performance benefits of processing within the network. In this talk, I explore the potential use of commodity programmable switches for in-network classification, by mapping trained machine learning models to match-action pipelines. I introduce IIsy, a software and hardware based prototype of our approach, and discuss the suitability of mapping to different targets. Our solution can be generalized to additional machine learning algorithms, using the methods presented in this work.


This talk is part of the Computer Laboratory Systems Research Group Seminar series.

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