Using Programmable Graphene Channels as Weights in Spin-Diffusive Neuromorphic Computing

Jiaxi Hu, Gordon Stecklein, Yoska Anugrah, Paul A. Crowell, Steven J. Koester

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

A graphene-based spin-diffusive neural network is presented in this paper that takes advantage of the locally tunable spin transport of graphene and the non-volatility of nanomagnets. By using electrostatically gated graphene as spintronic synapses, a weighted summation operation can be performed in the spin domain while the weights can be programmed using circuits in the charge domain. Four-component spin/charge circuit simulations coupled to magnetic dynamics are used to show the feasibility of the neuron-synapse functionality and quantify the analog weighting capability of the graphene under different spin-relaxation mechanisms. This spin-diffusive neural network using a graphene-based synapse design achieves total energy consumption of 0.55-0.97 fJ per cell \cdot synapse and attains significantly better scalability compared to its digital counterparts, particularly as the number and bit accuracy of the synapses increases.

Original languageEnglish (US)
Article number8334624
Pages (from-to)26-34
Number of pages9
JournalIEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Volume4
DOIs
StatePublished - Jun 2018

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

  • Analog weights
  • graphene
  • magnetic materials
  • neural networks
  • spin valves
  • spintronics

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