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。