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Demonstrating Distributed Inference in the User Plane with DUNE

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INFOCOM2025_DEMO_DSpace.pdf (878.8Kb)
Identificadores
URI: https://hdl.handle.net/20.500.12761/1905
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Autor(es)
Bütün, Beyza; de Andrés Hernández, David; Aguilar, Jose; Gucciardo, Michele; Fiore, Marco
Fecha
2025-05
Resumen
Deploying Machine Learning (ML) models in the user plane enables low-latency and scalable in-network inference, but integrating them into programmable devices faces stringent constraints in terms of memory resources and computing capabilities. In this demo, we show how the newly proposed DUNE, a novel framework for distributed user-plane inference across multiple programmable network devices by automating the decomposition of large ML models into smaller sub-models, mitigates the limitations of traditional monolithic ML designs. We run experiments on a testbed with Intel Tofino switches using measurement data and show how DUNE not only improves the accuracy that the traditional single-device monolithic approach gets but also maintains a comparable per-switch latency.
Compartir
Ficheros
INFOCOM2025_DEMO_DSpace.pdf (878.8Kb)
Identificadores
URI: https://hdl.handle.net/20.500.12761/1905
Metadatos
Mostrar el registro completo del ítem

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