With the ongoing integration of Machine Learning models into everyday life,
e.g. in the form of the Internet of Things (IoT), the evaluation of learned
models becomes more and more an important issue. Tree ensembles are one of the
best black-box classifiers available and routinely outperform more complex
classifiers. While the fast application of tree ensembles has already been
studied in the literature for Intel CPUs, they have not yet been studied in the
context of ARM CPUs which are more dominant for IoT applications. In this
paper, we convert the popular QuickScorer algorithm and its siblings from
Intel's AVX to ARM's NEON instruction set. Second, we extend our implementation
from ranking models to classification models such as Random Forests. Third, we
investigate the effects of using fixed-point quantization in Random Forests.
Our study shows that a careful implementation of tree traversal on ARM CPUs
leads to a speed-up of up to 9.4 compared to a reference implementation.
Moreover, quantized models seem to outperform models using floating-point
values in terms of speed in almost all cases, with a neglectable impact on the
predictive performance of the model. Finally, our study highlights
architectural differences between ARM and Intel CPUs and between different ARM
devices that imply that the best implementation depends on both the specific
forest as well as the specific device used for deployment.

本文研究基于 ARM CPU 的机器学习模型评价问题，并发现使用固定点量化方法可以在几乎所有情况下提高速度，同时对模型预测性能影响可以忽略不计。