In-network Neural Networks
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Abstract (arXiv)
We present N2Net, a system that implements binary neural networks using commodity switching chips deployed in network switches and routers. Our system shows that these devices can run simple neural network models, whose input is encoded in the network packets' header, at packet processing speeds (billions of packets per second). Furthermore, our experience highlights that switching chips could support even more complex models, provided that some minor and cheap modifications to the chip's design are applied. We believe N2Net provides an interesting building block for future end-to-end networked systems.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jan 2018.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 2; 2025: 1.
5 papers.
- 2024Brain-on-Switch: Towards Advanced Intelligent Network Data Plane via NN-Driven Traffic Analysis at Line-Speedcited by 5
- 2021FENXI: Deep-learning Traffic Analytics at the Edgecited by 4
- 2025Pegasus: A Universal Framework for Scalable Deep Learning Inference on the Dataplanecited by 2
- 2022Automating In-Network Machine Learningcited by 0
- 2024Optimal In-Network Distribution of Learning Functions for a Secure-by-Design Programmable Data Plane of Next-Generation Networkscited by 0
- 2025Pegasus: A Universal Framework for Scalable Deep Learning Inference on the Dataplane6 Jun 2025
- 2024Optimal In-Network Distribution of Learning Functions for a Secure-by-Design Programmable Data Plane of Next-Generation Networks27 Nov 2024
- 2024Brain-on-Switch: Towards Advanced Intelligent Network Data Plane via NN-Driven Traffic Analysis at Line-Speed17 Mar 2024
- 2022Automating In-Network Machine Learning18 May 2022
- 2021FENXI: Deep-learning Traffic Analytics at the Edge25 May 2021