混合CMOS-Memristor突触电路用于实现基于离子的可塑性模型
Jae Gwang Lim1,2, Sung-Jae Park1,3, Sang Min Lee1,3
1Center for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, 02792, South Korea.
Scientific reports
|August 2, 2024
概括
这项研究介绍了一种新的CMOS-memristor混合突触电路,能够模拟用于节能神经形态计算的多种尖峰时间依赖可塑性 (STDP) 规则. 该电路成功展示了关联式学习,为可扩展的大脑启发的AI铺平了道路.
科学领域:
- 神经形态工程的神经形态工程
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 神经形态计算的目标是节能大数据处理,尖端神经网络 (SNN) 和生物可信的学习规则是关键方法.
- 现有的SNN硬件经常实现标准的尖峰时机依赖可塑性 (STDP),但很少捕捉到生物大脑中观察到的多样化的STDP规则.
研究的目的:
- 提出和设计一个CMOS-memristor混合突触电路,用于硬件实现基于离子的可塑性模型.
- 使用这种混合电路模拟各种STDP曲线,超越标准STDP实现.
- 证明电路在神经网络操作中的能力,特别是关联式学习.
主要方法:
- 开发了一种CMOS-memristor混合突触电路,利用memristors进行模拟非挥发性内存.
- 设计了四个子块的电路,利用memristor属性来模拟可塑性.
- 采用H桥电路结构和PWM调制用于重量变化.
- 实施了基于离子的可塑性模型,以实现多种STDP曲线.
主要成果:
- 在单个CMOS-memristor混合电路中成功模拟了各种STDP曲线.
- 演示了一个简单的神经网络操作,用于协会学习 (帕夫洛维安调节).
- 用memristor的非挥发性和模拟性来利用高效的突触重量更新.
结论:
- 拟议的CMOS-memristor混合突触电路有效地模拟了各种STDP规则,解决了当前SNN硬件中的差距.
- 这种电路有助于开发出更具生物现实的神经形态系统.
- 该设计显示了可扩展的神经形态计算应用程序的前景,因为它具有大规模集成的潜力.
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