# MonoSparse-CAM: Efficient Tree Model Processing via Monotonicity and Sparsity in CAMs

arXiv: https://arxiv.org/abs/2407.11071 · HTML: https://mlsanity.com/paper/2407.11071/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 12 Jul 2024.

## Builds on (most cited first, 11 of 11)

- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [Why do tree-based models still outperform deep learning on tabular data?](https://mlsanity.com/paper/2207.08815/) (2022)
- [PyTorch Tabular: A Framework for Deep Learning with Tabular Data](https://mlsanity.com/paper/2104.13638/) (2021)
- [Network On Network for Tabular Data Classification in Real-world Applications](https://mlsanity.com/paper/2005.10114/) (2020)
- [LocalGLMnet: interpretable deep learning for tabular data](https://mlsanity.com/paper/2107.11059/) (2021)
- [A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends](https://mlsanity.com/paper/2209.06399/) (2022)
- [DIWIFT: Discovering Instance-wise Influential Features for Tabular Data](https://mlsanity.com/paper/2207.02773/) (2022)
- [A Federated Learning Benchmark on Tabular Data: Comparing Tree-Based Models and Neural Networks](https://mlsanity.com/paper/2405.02074/) (2024)

## Abstract (arXiv)

While the tree-based machine learning (TBML) models exhibit superior performance compared to neural networks on tabular data and hold promise for energy-efficient acceleration using aCAM arrays, their ideal deployment on hardware with explicit exploitation of TBML structure and aCAM circuitry remains a challenging task. In this work, we present MonoSparse-CAM, a new CAM-based optimization technique that exploits TBML sparsity and monotonicity in CAM circuitry to further advance processing performance. Our results indicate that MonoSparse-CAM reduces energy consumption by upto to 28.56x compared to raw processing and by 18.51x compared to state-of-the-art techniques, while improving the efficiency of computation by at least 1.68x.