# In-network Neural Networks

arXiv: https://arxiv.org/abs/1801.05731 · HTML: https://mlsanity.com/paper/1801.05731/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2018.

## Built on by (most cited first, 5 of 5)

- [FENXI: Deep-learning Traffic Analytics at the Edge](https://mlsanity.com/paper/2105.11738/) (2021)
- [Brain-on-Switch: Towards Advanced Intelligent Network Data Plane via NN-Driven Traffic Analysis at Line-Speed](https://mlsanity.com/paper/2403.11090/) (2024)
- [Pegasus: A Universal Framework for Scalable Deep Learning Inference on the Dataplane](https://mlsanity.com/paper/2506.05779/) (2025)
- [Automating In-Network Machine Learning](https://mlsanity.com/paper/2205.08824/) (2022)
- [Optimal In-Network Distribution of Learning Functions for a Secure-by-Design Programmable Data Plane of Next-Generation Networks](https://mlsanity.com/paper/2411.18384/) (2024)

## 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.