大脑启发的储计算使用可调节动力学和短期可塑性的memristors
Nicholas X Armendarez1, Ahmed S Mohamed1, Anurag Dhungel2
1Department of Mechanical Engineering, The Pennsylvania State University, 336 Reber Building, University Park, Pennsylvania 16802, United States.
ACS applied materials & interfaces
|January 25, 2024
概括
研究人员开发了用于储库计算 (RC) 的新型memristor. 这些独特的memristor能够以更少的能量进行更准确的数据处理,为先进的物理计算系统铺平了道路.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机工程 计算机工程
- 计算神经科学是一种神经科学.
背景情况:
- 储计算 (RC) 需要物理模拟设备来有效处理信息.
- 动态memristor由于其非线性和内存动态性,对RC来说非常有前途.
- 使用相同memristor的现有RC实现缺乏丰富的状态空间,需要复杂的解决方案.
研究的目的:
- 在基于离子通道的memristors中展示可控制和多样化的动态,以提高RC性能.
- 为了研究由不同的memristors组成的储层层的有效性,以提高信号处理.
- 建立下一代物理RC系统的基础,利用多样化的memristor动态.
主要方法:
- 使用基于离子通道的memristors,具有电压依赖的动态.
- 通过电压或离子通道度调整可控调整的memristor动态.
- 在模拟和实验中,在模拟和实验中构建并测试了具有少量不同memristor的储层层.
主要成果:
- 通过使用多种memristor储存器的单一数据编码,实现了显著更高的预测和分类准确性.
- 在使用五个不同的memristor进行非线性动态系统预测任务中证明了1.5 × 10-3的正常化平均平方误差.
- 在仅使用三种不同的memristor的储库进行神经活动分类任务时,获得了96.5%的准确性.
结论:
- 用少量独特的memristor构建的水库层在时间任务中提供了卓越的性能.
- 可控制的memristor动态使得能够创建多样化的储属性,以增强信号处理.
- 这项研究通过利用各种memristor组件的复杂动态来推进物理RC系统的发展.
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