将基于Transformer的粒子追踪模型部署到FPGA上，实现低延迟物理数据分析。
TrackCore-F: Deploying Transformer-Based Subatomic Particle Tracking on FPGAs
- 提出针对FPGA优化的Transformer模型合成方法，支持完整或分块部署。
- 在粒子追踪任务中验证了模型在FPGA上的可行性与低延迟性能。
- 适合高能物理实时分析、硬件加速研究者参考。
Transformer机器学习架构近年来发展迅速，已在高能物理中的喷注识别和粒子轨迹重建等任务中取得显著进展。同时，专用硬件加速器（尤其是FPGA）能有效实现在线或准在线延迟处理。然而，将基于Transformer的ML模型部署到FPGA仍面临挑战，现有工具支持有限，且FPGA资源受限。仅小型模型可直接部署，大型模型需以有意义甚至自动化的方式进行分割。本文致力于开发面向推理的Transformer模型单体或分块合成方法与工具，以解决上述问题。主要应用案例来自TrackFormers项目中的两个追踪模型设计。我们阐述了开发路径，展示初步结果，并进行性能对比。
原文摘要 · Abstract (English)
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.
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