Structured, or tabular, data is the most common format in data science. While
deep learning models have proven formidable in learning from unstructured data
such as images or speech, they are less accurate than simpler approaches when
learning from tabular data. In contrast, modern tree-based Machine Learning
(ML) models shine in extracting relevant information from structured data. An
essential requirement in data science is to reduce model inference latency in
cases where, for example, models are used in a closed loop with simulation to
accelerate scientific discovery. However, the hardware acceleration community
has mostly focused on deep neural networks and largely ignored other forms of
machine learning. Previous work has described the use of an analog content
addressable memory (CAM) component for efficiently mapping random forests. In
this work, we focus on an overall analog-digital architecture implementing a
novel increased precision analog CAM and a programmable network on chip
allowing the inference of state-of-the-art tree-based ML models, such as
XGBoost and CatBoost. Results evaluated in a single chip at 16nm technology
show 119x lower latency at 9740x higher throughput compared with a
state-of-the-art GPU, with a 19W peak power consumption.

在数据科学领域，结构化或表格形式的数据是最常见的格式。然而，在机器学习模型从表格中学习时，相比于简单的方法，深度学习模型的准确性要低得多。本文提出一种模拟 - 数字一体化架构，通过实现全新的高精度模拟内容寻址内存和可编程片上网络，实现了一种高效地实现基于树的机器学习模型（如 XGBoost 和 CatBoost）推理的方案。在 16 纳米技术完成的单芯片上，性能相比于最先进的 GPU 具有 119 倍的较低时延和 9740 倍的更高吞吐量，能够极大地加速科学发现。