# Deep Random Forest with Ferroelectric Analog Content Addressable Memory

arXiv: https://arxiv.org/abs/2110.02495 · HTML: https://mlsanity.com/paper/2110.02495/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2021.

## Built on by (most cited first, 5 of 5)

- [DT2CAM: A Decision Tree to Content Addressable Memory Framework](https://mlsanity.com/paper/2204.06114/) (2022)
- [RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory](https://mlsanity.com/paper/2506.05994/) (2025)
- [Trustworthy Tree-based Machine Learning by MoS2 Flash-based Analog CAM with Inherent Soft Boundaries](https://mlsanity.com/paper/2507.12384/) (2025)
- [CAMformer: Associative Memory is All You Need](https://mlsanity.com/paper/2511.19740/) (2025)
- [Probabilistic Tree Inference Enabled by FDSOI Ferroelectric FETs](https://mlsanity.com/paper/2604.05115/) (2026)

## Abstract (arXiv)

Deep random forest (DRF), which incorporates the core features of deep learning and random forest (RF), exhibits comparable classification accuracy, interpretability, and low memory and computational overhead when compared with deep neural networks (DNNs) in various information processing tasks for edge intelligence. However, the development of efficient hardware to accelerate DRF is lagging behind its DNN counterparts. The key for hardware acceleration of DRF lies in efficiently realizing the branch-split operation at decision nodes when traversing a decision tree. In this work, we propose to implement DRF through simple associative searches realized with ferroelectric analog content addressable memory (ACAM). Utilizing only two ferroelectric field effect transistors (FeFETs), the ultra-compact ACAM cell can perform a branch-split operation with an energy-efficient associative search by storing the decision boundaries as the analog polarization states in an FeFET. The DRF accelerator architecture and the corresponding mapping of the DRF model to the ACAM arrays are presented. The functionality, characteristics, and scalability of the FeFET ACAM based DRF and its robustness against FeFET device non-idealities are validated both in experiments and simulations. Evaluation results show that the FeFET ACAM DRF accelerator exhibits 10^6x/16x and 10^6x/2.5x improvements in terms of energy and latency when compared with other deep random forest hardware implementations on the state-of-the-art CPU/ReRAM, respectively.