# Trustworthy Tree-based Machine Learning by MoS2 Flash-based Analog CAM with Inherent Soft Boundaries

arXiv: https://arxiv.org/abs/2507.12384 · HTML: https://mlsanity.com/paper/2507.12384/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 50 references that are in the map.
- First posted on arXiv 16 Jul 2025.

## Builds on (most cited first, 10 of 10)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [End-to-end Autonomous Driving: Challenges and Frontiers](https://mlsanity.com/paper/2306.16927/) (2023)
- [Distilling a Neural Network Into a Soft Decision Tree](https://mlsanity.com/paper/1711.09784/) (2017)
- [Physical Adversarial Attack meets Computer Vision: A Decade Survey](https://mlsanity.com/paper/2209.15179/) (2022)
- [The Three Ghosts of Medical AI: Can the Black-Box Present Deliver?](https://mlsanity.com/paper/2012.06000/) (2020)
- [Deep Random Forest with Ferroelectric Analog Content Addressable Memory](https://mlsanity.com/paper/2110.02495/) (2021)
- [COSIME: FeFET based Associative Memory for In-Memory Cosine Similarity Search](https://mlsanity.com/paper/2207.12188/) (2022)
- [X-TIME: An in-memory engine for accelerating machine learning on tabular data with CAMs](https://mlsanity.com/paper/2304.01285/) (2023)

## Abstract (arXiv)

The rapid advancement of artificial intelligence has raised concerns regarding its trustworthiness, especially in terms of interpretability and robustness. Tree-based models like Random Forest and XGBoost excel in interpretability and accuracy for tabular data, but scaling them remains computationally expensive due to poor data locality and high data dependence. Previous efforts to accelerate these models with analog content addressable memory (CAM) have struggled, due to the fact that the difficult-to-implement sharp decision boundaries are highly susceptible to device variations, which leads to poor hardware performance and vulnerability to adversarial attacks. This work presents a novel hardware-software co-design approach using MoS2 Flash-based analog CAM with inherent soft boundaries, enabling efficient inference with soft tree-based models. Our soft tree model inference experiments on MoS2 analog CAM arrays show this method achieves exceptional robustness against device variation and adversarial attacks while achieving state-of-the-art accuracy. Specifically, our fabricated analog CAM arrays achieve 96% accuracy on Wisconsin Diagnostic Breast Cancer (WDBC) database, while maintaining decision explainability. Our experimentally calibrated model validated only a 0.6% accuracy drop on the MNIST dataset under 10% device threshold variation, compared to a 45.3% drop for traditional decision trees. This work paves the way for specialized hardware that enhances AI's trustworthiness and efficiency.