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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