Reservoir Computing Using Diffusive Memristors
Reservoir Computing Using Diffusive Memristors
复制标题
DOI：
10.1002/aisy.201900084
复制
发表时间：
2019-11-01
影响因子：
7.4
通讯作者：
Yang, J. Joshua
中科院分区：
文献类型：
--
作者：
Midya, Rivu;Wang, Zhongrui;Yang, J. Joshua
水库计算（RC）是一个框架，可以从一个时间输入到一个更高的维度的特征空间提取特征。水库后面是一个读出层，可以分析提取的特征，以完成推理和分类等任务。RC系统固有地表现出优势，因为训练仅在读出层执行，因此它们能够以低训练成本计算复杂的时间数据。本文中，实验性地实现了使用基于扩散忆阻器的储层和基于漂移忆阻器的读出层的物理储层计算系统。丰富的非线性动态行为所表现出的扩散忆阻器由于银迁移和强大的漂移忆阻器阵列的原位训练，使组合系统的理想时间模式分类。实验结果表明，RC系统可以成功地识别手写数字从修改后的美国国家标准与技术研究所（MNIST）数据集，达到83%的准确率。
Reservoir computing (RC) is a framework that can extract features from a temporal input into a higher-dimension feature space. The reservoir is followed by a readout layer that can analyze the extracted features to accomplish tasks such as inference and classification. RC systems inherently exhibit an advantage, since the training is only performed at the readout layer, and therefore they are able to compute complicated temporal data with a low training cost. Herein, a physical reservoir computing system using diffusive memristor-based reservoir and drift memristor-based readout layer is experimentally implemented. The rich nonlinear dynamic behavior exhibited by a diffusive memristor due to Ag migration and the robust in situ training of drift memristor arrays makes the combined system ideal for temporal pattern classification. It is then demonstrated experimentally that the RC system can successfully identify handwritten digits from the Modified National Institute of Standards and Technology (MNIST) dataset, achieving an accuracy of 83%.