Nature Communications (Jun 2020)

Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks

  • Rohit Abraham John,
  • Jyotibdha Acharya,
  • Chao Zhu,
  • Abhijith Surendran,
  • Sumon Kumar Bose,
  • Apoorva Chaturvedi,
  • Nidhi Tiwari,
  • Yang Gao,
  • Yongmin He,
  • Keke K. Zhang,
  • Manzhang Xu,
  • Wei Lin Leong,
  • Zheng Liu,
  • Arindam Basu,
  • Nripan Mathews

DOI
https://doi.org/10.1038/s41467-020-16985-0
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 9

Abstract

Read online

Accomplishing complex cognitive tasks such as speech recognition calls for artificial intelligence hardware with high computing precision. John et al. propose deep recurrent neural networks based on optoelectronic transition metal dichalcogenide memristors with high weight precision for in-memory computing.