# Fast Jet Tagging with MLP-Mixers on FPGAs

arXiv: https://arxiv.org/abs/2503.03103 · HTML: https://mlsanity.com/paper/2503.03103/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 5 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Interaction Networks for Learning about Objects, Relations and Physics](https://mlsanity.com/paper/1612.00222/) (2016)
- [AutoQ: Automated Kernel-Wise Neural Network Quantization](https://mlsanity.com/paper/1902.05690/) (2019)
- [Jet Constituents for Deep Neural Network Based Top Quark Tagging](https://mlsanity.com/paper/1704.02124/) (2017)

## Built on by (most cited first, 9 of 9)

- [hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware](https://mlsanity.com/paper/2512.01463/) (2025)
- [da4ml: Distributed Arithmetic for Real-time Neural Networks on FPGAs](https://mlsanity.com/paper/2507.04535/) (2025)
- [Sub-microsecond Transformers for Jet Tagging on FPGAs](https://mlsanity.com/paper/2510.24784/) (2025)
- [AIE4ML: An End-to-End Framework for Compiling Neural Networks for the Next Generation of AMD AI Engines](https://mlsanity.com/paper/2512.15946/) (2025)
- [JetFormer: A Scalable and Efficient Transformer for Jet Tagging from Offline Analysis to FPGA Triggers](https://mlsanity.com/paper/2601.17215/) (2026)
- [Towards Tensor Network Models for Low-Latency Jet Tagging on FPGAs](https://mlsanity.com/paper/2601.10801/) (2026)
- [Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems](https://mlsanity.com/paper/2507.19205/) (2025)
- [PQuantML: A Tool for End-to-End Hardware-aware Model Compression](https://mlsanity.com/paper/2603.26595/) (2026)
- [FQTree: Fine-grained Quantization and Hardware Generation of Boosted Decision Trees](https://mlsanity.com/paper/2608.12140/) (2026)

## Abstract (arXiv)

We explore the innovative use of MLP-Mixer models for real-time jet tagging and establish their feasibility on resource-constrained hardware like FPGAs. MLP-Mixers excel in processing sequences of jet constituents, achieving state-of-the-art performance on datasets mimicking Large Hadron Collider conditions. By using advanced optimization techniques such as High-Granularity Quantization and Distributed Arithmetic, we achieve unprecedented efficiency. These models match or surpass the accuracy of previous architectures, reduce hardware resource usage by up to 97%, double the throughput, and half the latency. Additionally, non-permutation-invariant architectures enable smart feature prioritization and efficient FPGA deployment, setting a new benchmark for machine learning in real-time data processing at particle colliders.