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,...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61980-y |
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| _version_ | 1849331631819587584 |
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| author | Minsuk Song Ryun-Han Koo Jangsaeng Kim Chang-Hyeon Han Jiyong Yim Jonghyun Ko Sijung Yoo Duk-hyun Choe Sangwook Kim Wonjun Shin Daewoong Kwon |
| author_facet | Minsuk Song Ryun-Han Koo Jangsaeng Kim Chang-Hyeon Han Jiyong Yim Jonghyun Ko Sijung Yoo Duk-hyun Choe Sangwook Kim Wonjun Shin Daewoong Kwon |
| author_sort | Minsuk Song |
| collection | DOAJ |
| description | 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 incremental step pulse programming technology to achieve efficient and scalable probabilistic weight control. The page-level programming capabilities and intrinsic device-to-device variations enable gaussian weight distributions in a single programming step, without structural modifications. By modulating the incremental step pulse programming voltage step, we achieve precise weight distribution control. The proposed system demonstrates successful uncertainty estimation, enhanced energy efficiency, and robustness to external noise for medical images. |
| format | Article |
| id | doaj-art-2a54777f7fe540eaa81087f03973d1fe |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-2a54777f7fe540eaa81087f03973d1fe2025-08-20T03:46:28ZengNature PortfolioNature Communications2041-17232025-07-0116111410.1038/s41467-025-61980-yFerroelectric NAND for efficient hardware bayesian neural networksMinsuk Song0Ryun-Han Koo1Jangsaeng Kim2Chang-Hyeon Han3Jiyong Yim4Jonghyun Ko5Sijung Yoo6Duk-hyun Choe7Sangwook Kim8Wonjun Shin9Daewoong Kwon10Department of Nanoscale Semiconductor Engineering, Hanyang UniversityDepartment of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National UniversityDepartment of Electronic Engineering, Sogang UniversityDepartment of Electrical Engineering, Hanyang UniversityDepartment of Electrical Engineering, Hanyang UniversityDepartment of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National UniversityThin Film Technical Unit, Device Research Center (DRC), Samsung Advanced Institute of TechnologyThin Film Technical Unit, Device Research Center (DRC), Samsung Advanced Institute of TechnologyThin Film Technical Unit, Device Research Center (DRC), Samsung Advanced Institute of TechnologyDepartment of Semiconductor Convergence Engineering, Sungkyunkwan UniversityDepartment of Nanoscale Semiconductor Engineering, Hanyang UniversityAbstract 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 incremental step pulse programming technology to achieve efficient and scalable probabilistic weight control. The page-level programming capabilities and intrinsic device-to-device variations enable gaussian weight distributions in a single programming step, without structural modifications. By modulating the incremental step pulse programming voltage step, we achieve precise weight distribution control. The proposed system demonstrates successful uncertainty estimation, enhanced energy efficiency, and robustness to external noise for medical images.https://doi.org/10.1038/s41467-025-61980-y |
| spellingShingle | Minsuk Song Ryun-Han Koo Jangsaeng Kim Chang-Hyeon Han Jiyong Yim Jonghyun Ko Sijung Yoo Duk-hyun Choe Sangwook Kim Wonjun Shin Daewoong Kwon Ferroelectric NAND for efficient hardware bayesian neural networks Nature Communications |
| title | Ferroelectric NAND for efficient hardware bayesian neural networks |
| title_full | Ferroelectric NAND for efficient hardware bayesian neural networks |
| title_fullStr | Ferroelectric NAND for efficient hardware bayesian neural networks |
| title_full_unstemmed | Ferroelectric NAND for efficient hardware bayesian neural networks |
| title_short | Ferroelectric NAND for efficient hardware bayesian neural networks |
| title_sort | ferroelectric nand for efficient hardware bayesian neural networks |
| url | https://doi.org/10.1038/s41467-025-61980-y |
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