# TrackCore-F: Deploying Transformer-Based Subatomic Particle Tracking on FPGAs

arXiv: https://arxiv.org/abs/2509.26335 · HTML: https://mlsanity.com/paper/2509.26335/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [HG-PIPE: Vision Transformer Acceleration with Hybrid-Grained Pipeline](https://mlsanity.com/paper/2407.17879/) (2024)
- [SWAT: Scalable and Efficient Window Attention-based Transformers Acceleration on FPGAs](https://mlsanity.com/paper/2405.17025/) (2024)
- [Reduced Simulations for High-Energy Physics, a Middle Ground for Data-Driven Physics Research](https://mlsanity.com/paper/2309.03780/) (2023)
- [TrackFormers: In Search of Transformer-Based Particle Tracking for the High-Luminosity LHC Era](https://mlsanity.com/paper/2407.07179/) (2024)
- [A Runtime-Adaptive Transformer Neural Network Accelerator on FPGAs](https://mlsanity.com/paper/2411.18148/) (2024)

## Abstract (arXiv)

The Transformer Machine Learning (ML) architecture has been gaining considerable momentum in recent years. In particular, computational High-Energy Physics tasks such as jet tagging and particle track reconstruction (tracking), have either achieved proper solutions, or reached considerable milestones using Transformers. On the other hand, the use of specialised hardware accelerators, especially FPGAs, is an effective method to achieve online, or pseudo-online latencies. The development and integration of Transformer-based ML to FPGAs is still ongoing and the support from current tools is very limited or non-existent. Additionally, FPGA resources present a significant constraint. Considering the model size alone, while smaller models can be deployed directly, larger models are to be partitioned in a meaningful and ideally, automated way. We aim to develop methodologies and tools for monolithic, or partitioned Transformer synthesis, specifically targeting inference. Our primary use-case involves two machine learning model designs for tracking, derived from the TrackFormers project. We elaborate our development approach, present preliminary results, and provide comparisons.