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通过双门设计来实现晶体管级激活功能:从模拟西格和高斯控制到实时硬件演示
Junhyung Cho1, Youngmin Han2, Won Woo Lee1
1Department of Artificial Intelligence Semiconductor Engineering, Hanyang University, 222 Wangsimni-ro, Seoul, 04763, Republic of Korea.
Advanced materials (Deerfield Beach, Fla.)
|November 24, 2025
概括
新的晶体管使节能的人工智能 (AI) 硬件可调节的模拟激活功能. 这些设备精确地控制了西格和高斯函数,提高了AI模型的准确性并降低了功耗.
科学领域:
- 神经形态工程的神经形态工程
- 固态设备 固态设备
- 人工智能 硬件 硬件
背景情况:
- 节能的人工智能 (AI) 硬件依赖于可调的模拟激活功能.
- 现有的解决方案往往缺乏对激活函数参数的精确控制.
研究的目的:
- 引入可调节模拟激活功能的新型晶体管设计.
- 为了证明设备层面对西格和高斯函数参数的控制.
- 为了验证这些晶体管在AI硬件中的系统级性能.
主要方法:
- 使用屏幕门结构开发类似于西格的激活功能晶体管 (SA-晶体管) 和类似于高斯的激活功能晶体管 (GA-晶体管).
- 通过屏幕门电压精确调整激活功能的参数 (斜率,和,振幅,标准偏差).
- 将晶体管集成到基于硬件的多层感知器 (MLP) 中,用于系统级验证.
主要成果:
- SA-晶体管和GA-晶体管证明了模拟激活参数的精确和连续调整性.
- 使用SA晶体管,肺部MRI分类准确度从77%提高到84%.
- 使用GA晶体管,改进时间序列预测R2从0.82到0.93.
- 使用基于硬件的MLP,在IRIS数据集上实现了96.7%的准确性.
结论:
- 可调的模拟激活晶体管为硬件优化的神经计算提供了一条途径.
- 这些设备使神经形态加速器具有较低的电路复杂性和功耗.
- 系统级验证证实了在没有数字后处理的情况下实现高分类准确性的潜力.
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