低能耗和可调节的LIF神经元使用SiGe带隙设计的电阻开关晶体管
Yijoon Kim1, Hyangwoo Kim2, Kyounghwan Oh3
1Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea.
Discover nano
|August 23, 2024
概括
我们使用一种新型晶体管开发了一种低能耗泄漏的整合与发射 (LIF) 神经元. 这种设计大大降低了神经网络应用程序的能量消耗.
科学领域:
- 神经形态工程的神经形态工程
- 固态电子 固态电子
- 人工智能 硬件 硬件
背景情况:
- 尖端神经网络 (SNN) 需要节能的神经元模型.
- 传统的神经元设计通常依赖于复杂的外部电路,增加功耗.
- 电阻开关晶体管 (RST) 为紧和低功耗神经元实现提供了潜力.
研究的目的:
- 提出一种新的漏洞整合和发射 (LIF) 神经元,能耗降低,功能可调.
- 为了证明在神经元电路中带隙工程电阻开关晶体管 (BE-RST) 的有效性.
- 为了使神经网络应用程序能够节能运行.
主要方法:
- 设计了一个LIF神经元电路,包括一个BE-RST,一个电容器和一个电阻.
- 在BE-RST中使用-异质连接来增强歇斯底里的电流切换.
- 研究了门偏差对泄漏率和物业调节门的影响.
主要成果:
- 与纯-RST神经元 (2.08 pJ/spike) 相比,实现了0.36 pJ/spike的能量消耗,减少了6倍.
- 通过网关偏差调制证明了可调节的尖端特性.
- 验证了神经元适用于节能稀疏活动和高学习精度的适用性.
结论:
- 拟议的基于BE-RST的LIF神经元提供了显著的能源节约.
- 可调节的功能允许在各种SNN应用中进行优化.
- 这种神经元设计是下一代神经形态计算的有希望的候选者.
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