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.
@article{arxiv.2503.03103,
title = {Fast Jet Tagging with MLP-Mixers on FPGAs},
author = {Chang Sun and Jennifer Ngadiuba and Maurizio Pierini and Maria Spiropulu},
journal= {arXiv preprint arXiv:2503.03103},
year = {2025}
}