There has been considerable interest and resulting progress in implementing
machine learning (ML) models in hardware over the last several years from the
particle and nuclear physics communities. A big driver has been the release of
the Python package, hls4ml, which has enabled porting models specified and
trained using Python ML libraries to register transfer level (RTL) code. So
far, the primary end targets have been commercial FPGAs or synthesized custom
blocks on ASICs. However, recent developments in open-source embedded FPGA
(eFPGA) frameworks now provide an alternate, more flexible pathway for
implementing ML models in hardware. These customized eFPGA fabrics can be
integrated as part of an overall chip design. In general, the decision between
a fully custom, eFPGA, or commercial FPGA ML implementation will depend on the
details of the end-use application. In this work, we explored the parameter
space for eFPGA implementations of fully-connected neural network (fcNN) and
boosted decision tree (BDT) models using the task of neutron/gamma
classification with a specific focus on resource efficiency. We used data
collected using an AmBe sealed source incident on Stilbene, which was optically
coupled to an OnSemi J-series SiPM to generate training and test data for this
study. We investigated relevant input features and the effects of
bit-resolution and sampling rate as well as trade-offs in hyperparameters for
both ML architectures while tracking total resource usage. The performance
metric used to track model performance was the calculated neutron efficiency at
a gamma leakage of 10$^{-3}$. The results of the study will be used to aid the
specification of an eFPGA fabric, which will be integrated as part of a test
chip.

实施机器学习模型于硬件上已取得了相当大的兴趣和进展，而该研究探索了电子可变程序逻辑阵列（eFPGA）在完全连接神经网络（fcNN）和增强决策树（BDT）模型实现方面的参数空间以及资源效率的权衡，以帮助制定一个作为测试芯片的一部分的 eFPGA 结构的规范。

研究面向 eFPGA 的资源高效中子 /γ 射线分类机器学习模型

Investigating Resource-efficient Neutron/Gamma Classification ML Models  Targeting eFPGAs

This work introduces a highly efficient implementation of the transformer
architecture on a Field-Programmable Gate Array (FPGA) by using the
\texttt{hls4ml} tool. Given the demonstrated effectiveness of transformer
models in addressing a wide range of problems, their application in
experimental triggers within particle physics becomes a subject of significant
interest. In this work, we have implemented critical components of a
transformer model, such as multi-head attention and softmax layers. To evaluate
the effectiveness of our implementation, we have focused on a particle physics
jet flavor tagging problem, employing a public dataset. We recorded latency
under 2 $\mu$s on the Xilinx UltraScale+ FPGA, which is compatible with
hardware trigger requirements at the CERN Large Hadron Collider experiments.

本文介绍了在可编程门阵列 (FPGA) 上使用 hls4ml 工具实现变压器架构的高效实现。通过使用变压器模型在解决各种问题方面的有效性的证明，其在粒子物理实验触发器中的应用成为了一个引人关注的课题。在这项工作中，我们实现了变压器模型的关键组成部分，如多头注意力和 softmax 层。为了评估我们的实现的有效性，我们专注于一个粒子物理学喷注风味标记问题，并使用了一个公共数据集。我们在 Xilinx UltraScale+ FPGA 上记录了低于 2 微秒的延迟，该延迟符合 CERN 大型强子对撞机实验的硬件触发器要求。