使用 3D 16 层 Fe-二极管阵列的统一源和突触权重的贝叶斯神经网络
Yuanquan Huang1,2,3, Qiqiao Wu4,5, Tiancheng Gong6,7
1State Key Laboratory of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China.
Nature communications
|August 28, 2025
概括
铁二极管设备提供了一个稳定的高频源,非常适合边缘人工智能 (AI) 系统. 这些设备使得即使在极端温度和高频率下也能进行高效,准确的AI计算.
科学领域:
- 材料科学
- 人工智能
- 设备物理
背景情况:
- 边缘人工智能系统需要高频操作, 挑战与频率降低的传统源.
- 现有的源在不同温度和频率下难以达到边缘AI所需的稳定性.
研究的目的:
- 调查Fe-二极管设备作为边缘AI的稳定高频源的适用性.
- 在基于芯片的Fe-二极管设备上实验实现贝叶斯神经网络.
主要方法:
- 对不同读取电压,频率和温度的Fe-二极管设备噪声密度的描述.
- 使用3D16层Fe-二极管阵列的层次贝叶斯神经网络的实验实现.
- 一个统一的源和四态突触架构的演示.
主要成果:
- 铁二极管设备具有稳定的噪声密度,可通过读取电压改变,跨高频率和温度波动.
- 实现的贝叶斯神经网络实现了高识别精度.
- 该系统表现出高面积效率和广泛的工作温度范围.
结论:
- 铁二极管设备在物理上适用于需要高频率和环境稳定的边缘AI应用.
- 开发的基于Fe-二极管的贝叶斯神经网络为边缘人工智能提供低能现场训练和高性能.
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