Year: 2025
Type: conference-paper
Abstract: 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.
Authors: BÜTÜN, BEYZA, de Andres Hernandez, David, Aguilar Polo, Josue Miguel, Gucciardo, Michele, Fiore, Marco
Institution: IMDEA Networks
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: European Commission
OpenAlex ID: W6930937181
FWCI:
Cites:
Cited by:
Related to: