[2604.05115] Probabilistic Tree Inference Enabled by FDSOI Ferroelectric FETs

Computer Science > Emerging Technologies arXiv:2604.05115 (cs) [Submitted on 6 Apr 2026] Title:Probabilistic Tree Inference Enabled by FDSOI Ferroelectric FETs Authors:Pengyu Ren, Xingtian Wang, Boyang Cheng, Jiahui Duan, Giuk Kim, Xuezhong Niu, Halid Mulaosmanovic, Stefan Duenkel, Sven Beyer, X. Sharon Hu, Ningyuan Cao, Kai Ni View a PDF of the paper titled Probabilistic Tree Inference Enabled by FDSOI Ferroelectric FETs, by Pengyu Ren and 11 other authors View PDF HTML (experimental) Abstract: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 complexity and energy overheads. Here we report a monolithic FDSOI-FeFET hardware platform that natively supports both ACAM and GRNG functionalities. The ferroelectric polarization of FeFETs enables compact, energy-efficient multi-bit storage for ACAM, and band-to-band tunneling in the gate-to-drain...