Open AccessReservoir Computing Using Diffusive Memristors
Author(s) -
Midya Rivu,
Wang Zhongrui,
Asapu Shiva,
Zhang Xumeng,
Rao Mingyi,
Song Wenhao,
Zhuo Ye,
Upadhyay Navnidhi,
Xia Qiangfei,
Yang J. Joshua
Publication year - 2019
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.201900084
Subject(s) - memristor , mnist database , reservoir computing , computer science , layer (electronics) , inference , nonlinear system , dimension (graph theory) , artificial intelligence , pattern recognition (psychology) , electronic engineering , deep learning , engineering , artificial neural network , materials science , mathematics , physics , nanotechnology , recurrent neural network , quantum mechanics , pure mathematics
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%.