Automated Transformer Deployment on FPGA for Particle Tracking
Abstract
The High-Luminosity Large Hadron Collider (HL-LHC) will significantly increase data processing
requirements for tasks such as particle tracking. Transformer-based models have recently shown
promising results for track reconstruction, but running such models in hardware environments
relevant to high-energy physics is far from straightforward. Strict latency constraints, limited
on-chip memory, and integration into existing systems all have to be considered.
This thesis explores how a Transformer-based tracking model can be deployed on an FPGA platform
under realistic resource and accuracy constraints. Instead of aiming for a full end-to-end
implementation, the focus is placed on accelerating a representative Transformer encoder layer and
studying the practical trade-offs that arise in hardware. An FPGA accelerator prototype is developed
using high-level synthesis, with particular attention to on-chip weight storage, memory
organization, and various hardware optimizations.
Beyond the hardware design itself, this work also addresses the practical challenge that deploying
machine learning models on FPGAs typically requires expertise in both deep learning and hardware
design. To lower this barrier, a semi-automated workflow is developed that enables selective
offloading Transformer model parts to the FPGA, while the remaining parts of the model execute on a
general-purpose processor. The goal is not only to build an accelerator, but also to make
experimentation with FPGA-based acceleration more accessible to researchers without extensive
hardware experience.
Experimental results show that the design is largely memory-bound, with on-chip memory usage forming
the main bottleneck. Multi-layer implementations improve throughput and energy efficiency, but the
achieved latency remains higher than that of modern GPU solutions. Overall, the results demonstrate
that selective acceleration of Transformer components on FPGA hardware is feasible and provide a
practical, reproducible workflow that helps bridge the gap between machine learning models and FPGA
deployment in a high-energy physics context.
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