铁电NAND用于高效的硬件贝叶斯神经网络
Minsuk Song1, Ryun-Han Koo2, Jangsaeng Kim3,4
1Department of Nanoscale Semiconductor Engineering, Hanyang University, Seoul, 04763, Republic of Korea.
Nature communications
|July 27, 2025
概括
我们开发了一种基于3D铁电NAND的贝叶斯神经网络系统,用于可靠的人工智能. 该系统有效量化AI模型中的不确定性,提高了噪音较大的医疗图像的性能.
科学领域:
- 人工智能的人工智能
- 神经形态计算是一种神经形态计算.
- 材料科学 材料科学 材料科学
背景情况:
- 传统的神经网络缺乏不确定性量化,限制了对真实世界的数据的可靠性.
- 贝叶斯神经网络通过将权重建成概率分布来提高稳定性.
- 贝叶斯神经网络的硬件实现在控制重量分布方面面临挑战.
研究的目的:
- 提出一个高效和可扩展的基于3D铁电NAND的贝叶斯神经网络系统.
- 为了实现精确的概率权重控制,提高AI可靠性.
- 证明系统在不确定性估计中的有效性和医疗图像分析的稳定性.
主要方法:
- 使用3D铁电NAND架构进行概率式重量控制.
- 采用增量步骤脉冲编程技术,以高效调整重量分布.
- 杆页面级编程和设备对设备的变化,用于高斯式重量分布.
主要成果:
- 使用铁电NAND实现了高效和可扩展的概率式重量控制.
- 通过调节编程电压来证明对重量分布的精确控制.
- 成功实现了不确定性估计和医疗图像的强化稳定性.
结论:
- 拟议的基于3D铁电NAND的贝叶斯神经网络系统为不确定性量化提供了有效的解决方案.
- 该系统增强了AI的稳定性和能源效率,特别是在医疗诊断等应用中.
- 这种方法克服了贝叶斯神经网络的硬件实现挑战.
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