High Performance of an In-Material Reservoir Computing Device Achieved by Complex Dynamics in a Nanoparticle Random Network Memristor-Reference-Cited by-同舟云学术

High Performance of an In-Material Reservoir Computing Device Achieved by Complex Dynamics in a Nanoparticle Random Network Memristor

Published:2023-12-26 Issue: Volume: Page:
ISSN:2637-6113
Container-title:ACS Applied Electronic Materials
language:en
Short-container-title:ACS Appl. Electron. Mater.

Author:

Srikimkaew Oradee¹^ORCID,Banerjee Deep¹²^ORCID,Azhari Saman¹²^ORCID,Usami Yuki¹²^ORCID,Tanaka Hirofumi¹²³^ORCID

Affiliation:

1. Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology (Kyutech), 2-4 Hibikino, Wakamatsu, Kitakyushu 808-0196, Japan

2. Research Center for Neuromorphic AI Hardware, Kyutech, Kitakyushu 808-0196, Japan

3. Institute of Science, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand

Funder

Thailand Science Research and Innovation

Core Research for Evolutional Science and Technology

ACT-X

Japan Society for the Promotion of Science

Kitakyushu Foundation for the Advancement of Industry, Science and Technology

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,Electrochemistry,Electronic, Optical and Magnetic Materials

Link

https://pubs.acs.org/doi/pdf/10.1021/acsaelm.3c01046

Reference41 articles.

1. Reservoir computing approaches to recurrent neural network training

2. Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication

3. An experimental unification of reservoir computing methods

4. Recent advances in physical reservoir computing: A review

5. Physical reservoir computing—an introductory perspective

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