# Reconfigurable Computing Challenge: Transformer for Jet Tagging on Versal AI Engines

arXiv: https://arxiv.org/abs/2606.17500 · HTML: https://mlsanity.com/paper/2606.17500/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 16 Jun 2026.

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

- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [I-BERT: Integer-only BERT Quantization](https://mlsanity.com/paper/2101.01321/) (2021)
- [Particle Transformer for Jet Tagging](https://mlsanity.com/paper/2202.03772/) (2022)
- [Sub-microsecond Transformers for Jet Tagging on FPGAs](https://mlsanity.com/paper/2510.24784/) (2025)
- [CAT: Customized Transformer Accelerator Framework on Versal ACAP](https://mlsanity.com/paper/2409.09689/) (2024)

## Abstract (arXiv)

Transformer-based models achieve strong performance for jet tagging at the CERN LHC, but deploying them in low-latency, resource-constrained trigger systems is challenging. We present an initial implementation of a quantized, integer-only transformer for jet tagging on the AMD Versal AI Engine (AIE), mapping dense and multi-head attention (MHA) layers to AIE tiles. The main contribution is a reusable software framework that represents transformer layers as composable AIE building blocks and automatically generates the corresponding Vitis graph code from a high-level Python model description. This framework provides a foundation for future research and is released as open-source software at https://github.com/KastnerRG/particle_transformer_aie.