MUTA: enabling multi-task neural network inference in programmable data-planes

Conference item

The need for real-time inference of large volumes of data led to the development of in-network machine learning. Programmable network switches can now execute various machine learning models in the data-plane at line rate. While a stream of data may require several prediction tasks, such as predicting bit rate, flow size, or traffic class, current solutions only support separate models for each task. This places a significant burden on the data-plane and leads to substantial resource consumption when deploying multiple tasks. To solve this problem, we introduce MUTA; a novel in-network multi-task learning solution. MUTA enables executing multiple inference tasks concurrently in the data-plane, without exhausting available resources. It introduces a data-plane mapping methodology to fit non-binarized multi-task neural networks within network switches. MUTA is deployed on P4-based hardware switches, and is shown to reduce memory requirements by ×10.5 and improve accuracy by up to 9.14% using limited training data, compared with state-of-the-art single-task learning solutions.

Authors: Zhang, K; Zheng, C; Samaan, N; Karmouch, A; Zilberman, N

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Publication date: 2025-06-19
• Acceptance date: 2025-03-21
• Event title: IEEE 26th Conference on High Performance Switching and Routing (HPSR 2025)
• Event location: Suita, Osaka, Japan
• Event website: https://hpsr2025.ieee-hpsr.org/
• Event start date: 2025-05-20
• Event end date: 2025-05-22
• DOI: 10.1109/HPSR64165.2025.11038854
• EISSN: 2325-5609
• ISSN: 2325-5595
• Language: English
• Keywords: in-network computing; multi-task learning; neural networks; programmable data-planes