Trustworthy tree-based machine learning by MoS2 flash-based analog content-addressable memory with inherent soft boundaries

The rapid advancement of artificial intelligence has raised concerns regarding its trustworthiness, especially in terms of interpretability and robustness. Tree-based models such as Random Forest 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 because difficult-to-implement sharp decision boundaries are highly susceptible to device variations, leading to poor hardware performance and vulnerability to adversarial attacks. Here, we present a hardware-software co-design approach using MoS2 flash-based analog CAM with inherent soft boundaries, enabling efficient inference with soft tree-based models. Our fabricated analog CAM arrays achieve 96% accuracy on the Wisconsin Diagnostic Breast Cancer dataset, and our experimentally calibrated model shows only a 0.6% accuracy drop on MNIST under 10% device threshold variation, compared with 45.3% for traditional decision trees. Scaling interpretable tree-based AI on analog content addressable memory is hindered by its inherent soft decision boundary and device variations. Wen et al. harness these probabilistic boundaries in a robust MoS2 array, achieving 96% recognition accuracy on the Wisconsin Diagnostic Breast Cancer dataset.