时空数据处理与memristor 交叉条数组基图库库
Yoon Ho Jang1, Soo Hyung Lee1, Janguk Han1
1Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
Advanced materials (Deerfield Beach, Fla.)
|October 25, 2023
概括
本研究引入了一种使用金属绝缘器金属设备处理时空数据的新型图库. 这种新方法有效地捕捉空间输入关系,改善复杂数据集的储库计算性能.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
- 神经科学是一个神经科学.
背景情况:
- 物理储库计算 (RC) 提供了一种用于并行处理复杂时空数据的强大方法.
- 传统的基于memristor的储存器由于其映射方案而难以捕捉时间变化的输入中的空间关系.
研究的目的:
- 引入一个物理图库,能够捕捉时间变化的输入之间的空间关系.
- 通过使用在对角交叉阵列 (mCBA) 上的金属电池和具有动态自校正的memristors来增强储水池计算.
主要方法:
- 开发了一个新的图形储库架构,使用在对角交叉阵列 (mCBA) 的金属单元.
- 输入和反向输入信号应用于mCBA的字和位行,使得memristors能够存储相关信息.
- 这个mCBA配置将时空相关性映射到一个高维特征空间中.
主要成果:
- 在Mackey-Glass时间序列预测中,mCBA图库实现了0.09的正常化根-平方平均误差.
- 它在MNIST手写数字识别中显示了97.21%的准确性.
- 该系统在人类连接组分类中实现了80.0%的诊断准确率.
结论:
- 开发的mCBA图库有效地捕捉了输入数据中的时空相关性.
- 这种方法显著提高了用于复杂数据处理任务的储库计算的性能.
- 这些发现表明了基于memristor的计算架构的有希望的新方向.
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