Ferroelectric NAND for efficient hardware bayesian neural networks.

ABSTRACT: The rapid advancement of artificial intelligence has enabled breakthroughs in diverse fields, including autonomous systems and medical diagnostics. However, conventional deterministic neural networks struggle to capture uncertainty, limiting their reliability when handling real-world data, which are often noisy, imbalanced, or scarce. Bayesian neural networks address this limitation by representing weights as probabilistic distributions, allowing for natural uncertainty quantification and improved robustness. Despite their advantages, hardware-based implementations face significant challenges due to the difficulty of independently tuning both the mean and variance of weight distributions. Herein, we propose a 3D ferroelectric NAND-based Bayesian neural network system that leverages increme

SUBMITTER: Song M 

PROVIDER: S-EPMC12297355 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature