Probabilistic Tree Inference Enabled by FDSOI Ferroelectric FETs
Artificial intelligence applications in autonomous driving, medical diagnostics, and financial systems increasingly demand machine learning models that can provide robust uncertainty quantification, interpretability, and noise resilience. Bayesian decision trees (BDTs) are attractive for these tasks because they combine probabilistic reasoning, interpretable decision-making, and robustness to noise. However, existing hardware implementations of BDTs based on CPUs and GPUs are limited by memory bottlenecks and irregular processing patterns, while multi-platform solutions exploiting analog content-addressable memory (ACAM) and Gaussian random number generators (GRNGs) introduce integration ...
| Authors | Pengyu Ren, Xingtian Wang, Boyang Cheng et al. |
|---|---|
| Primary category | cs.ET |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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