一个可重新配置的晶体管用于抗噪声静态尖端神经网络
Ho-Young Maeng1, Hyeonji Lee1, Sang-Won Lee1
1School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
ACS nano
|November 7, 2025
概括
这项研究引入了一种新的神经晶体管 (神经晶体管) 用于随机尖端神经网络 (SSNN). 该设备通过整合双重随机和决定性的功能,提高了神经形态系统中的噪声弹性和适应性.
科学领域:
- 神经形态工程的神经形态工程
- 材料科学 材料科学 材料科学
- 计算神经科学是一种神经科学.
背景情况:
- 尖端神经网络 (SNN) 提供能源效率,但缺乏生物随机性,限制噪声弹性.
- 随机SNN (SSNN) 通过结合概率行为来解决这一问题,以提高噪声耐受性和适应性.
研究的目的:
- 开发一种新型的神经晶体管 (神经晶体管) 具有双随机和确定性特性,用于先进的SSNNs.
- 为了证明神经电阻对抗噪声和节能神经形态计算的能力.
主要方法:
- 重新设计了传统的CMOS技术,以创建基于及其衍生物的神经电阻器.
- 在单个神经电阻装置内集成的随机编码 (输入层) 和漏洞集成和火 (LIF) 行为 (隐藏/输出层).
- 使用单晶体管锁 (STL) 机制,通过冲击电离 (静态) 和电荷积累 (LIF) 实现双模式操作.
主要成果:
- 神经电阻成功地将双重随机和决定性的功能集成到一个单一的设备中.
- 基于神经电阻的SSNN在MNIST数据集上实现了92%的分类准确性,高斯噪声为30%.
- 展示了强大的噪音弹性和可扩展,生物启发的神经形态系统的潜力.
结论:
- 开发的神经电阻器通过可重新配置的双模式操作实现了可靠和耐噪声的SSNN.
- 这一创新简化了电路设计,并提高了节能神经形态系统的可扩展性.
- 基于神经电阻的SSNN在需要抗噪强度的实际应用中显示出显著的前景.
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