Nonvolatile Spintronic Memory Cells for Neural Networks

Andrew W. Stephan, Qiuwen Lou, Michael T. Niemier, Xiaobo Sharon Hu, Steven J. Koester

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

A new spintronic nonvolatile memory cell analogous to 1T DRAM with non-destructive READ is proposed. The cells can be used as neural computing units. A dual-circuit neural network architecture is proposed to leverage these devices against the complex operations involved in convolutional networks. Simulations based on HSPICE and MATLAB were performed to study the performance of this architecture when classifying images as well as the effect of varying the size and stability of the nanomagnets. The spintronic cells outperform a purely charge-based implementation of the same network, consuming \approx 100 -pJ total energy per image processed.

Original languageEnglish (US)
Article number8786867
Pages (from-to)67-73
Number of pages7
JournalIEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Volume5
Issue number2
DOIs
StatePublished - Dec 2019

Bibliographical note

Funding Information:
This work was supported by Seagate Technology PLC.

Publisher Copyright:
© 2014 IEEE.

Keywords

  • CMOS
  • Cellular neural network (CeNN)
  • MNIST
  • Rashba-Edelstein
  • convolutional neural network (CoNN)
  • magnetoelectric
  • nonvolatile memory
  • spintronics

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