An All-in-One Bioinspired Neural Network

Shiva Subbulakshmi Radhakrishnan, Akhil Dodda, Saptarshi Das

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

In spite of recent advancements in artificial neural networks (ANNs), the energy efficiency, multifunctionality, adaptability, and integrated nature of biological neural networks remain largely unimitated by hardware neuromorphic computing systems. Here, we exploit optoelectronic, computing, and programmable memory devices based on emerging two-dimensional (2D) layered materials such as MoS2 to demonstrate a monolithically integrated, multipixel, and "all-in-one"bioinspired neural network (BNN) capable of sensing, encoding, learning, forgetting, and inferring at minuscule energy expenditure. We also demonstrate learning adaptability and simulate learning challenges under specific synaptic conditions to mimic biological learning. Our findings highlight the potential of in-memory computing and sensing based on emerging 2D materials, devices, and integrated circuits to not only overcome the bottleneck of von Neumann computing in conventional CMOS designs but also to aid in eliminating the peripheral components necessary for competing technologies such as memristors.

Original languageEnglish (US)
Pages (from-to)20100-20115
Number of pages16
JournalACS nano
Volume16
Issue number12
DOIs
StatePublished - Dec 27 2022

All Science Journal Classification (ASJC) codes

  • General Materials Science
  • General Engineering
  • General Physics and Astronomy

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