The spatiotemporal nature of neuronal behavior in spiking neural networks (SNNs) makes SNNs promising for edge applications that require high energy efficiency. To realize SNNs in hardware, spintronic neuron implementations can bring advantages of scalability and energy efficiency. Domain wall (DW)-based magnetic tunnel junction (MTJ) devices are well suited for probabilistic neural networks given their intrinsic integrate-and-fire behavior with tunable stochasticity. Here, we present a scaled DW-MTJ neuron with voltage-dependent firing probability. The measured behavior was used to simulate a SNN that attains accuracy during learning compared to an equivalent, but more complicated, multi-weight DW-MTJ device. The validation accuracy during training was also shown to be comparable to an ideal leaky integrate and fire device. However, during inference, the binary DW-MTJ neuron outperformed the other devices after Gaussian noise was introduced to the Fashion-MNIST classification task. This work shows that DW-MTJ devices can be used to construct noise-resilient networks suitable for neuromorphic computing on the edge.
REFERENCES
1.
V.
Milo
,
G.
Malavena
,
C. M.
Compagnoni
, and
D.
Ielmini
, “
Memristive and CMOS devices for neuromorphic computing
,” Materials
13
, 166
(2020
).2.
T.
Leonard
,
S.
Liu
,
M.
Alamdar
,
H.
Jin
,
C.
Cui
,
O. G.
Akinola
,
L.
Xue
,
T. P.
Xiao
,
J. S.
Friedman
,
M. J.
Marinella
,
C. H.
Bennett
, and
J. A. C.
Incorvia
, “
Shape‐dependent multi‐weight magnetic artificial synapses for neuromorphic computing
,” Adv. Electron. Mater.
8
, 2200563
(2022
).3.
D.
Kireev
,
S.
Liu
,
H.
Jin
,
T.
Patrick Xiao
,
C. H.
Bennett
,
D.
Akinwande
, and
J. A. C.
Incorvia
, “
Metaplastic and energy-efficient biocompatible graphene artificial synaptic transistors for enhanced accuracy neuromorphic computing
,” Nat. Commun.
13
, 4386
(2022
).4.
G.
Zhou
,
Z.
Wang
,
B.
Sun
,
F.
Zhou
,
L.
Sun
,
H.
Zhao
,
X.
Hu
,
X.
Peng
,
J.
Yan
,
H.
Wang
,
W.
Wang
,
J.
Li
,
B.
Yan
,
D.
Kuang
,
Y.
Wang
,
L.
Wang
, and
S.
Duan
, “
Volatile and nonvolatile memristive devices for neuromorphic computing
,” Adv. Electron. Mater.
8
, 2101127
(2022
).5.
S.
Kumar
,
X.
Wang
,
J. P.
Strachan
,
Y.
Yang
, and
W. D.
Lu
, “
Dynamical memristors for higher-complexity neuromorphic computing
,” Nat. Rev. Mater.
7
, 575
–591
(2022
).6.
D.
Ielmini
and
H.-S. P.
Wong
, “
In-memory computing with resistive switching devices
,” Nat. Electron.
1
, 333
–343
(2018
).7.
S.
Liu
,
T. P.
Xiao
,
J.
Kwon
,
B. J.
Debusschere
,
S.
Agarwal
,
J. A. C.
Incorvia
, and
C. H.
Bennett
, “
Bayesian neural networks using magnetic tunnel junction-based probabilistic in-memory computing
,” Front. Nanotechnol.
4
, 1021943
(2022
). 8.
J.
Grollier
,
D.
Querlioz
,
K. Y.
Camsari
,
K.
Everschor-Sitte
,
S.
Fukami
, and
M. D.
Stiles
, “
Neuromorphic spintronics
,” Nat. Electron.
3
, 360
–370
(2020
).9.
Q.
Yang
,
R.
Mishra
,
Y.
Cen
,
G.
Shi
,
R.
Sharma
,
X.
Fong
, and
H.
Yang
, “
Spintronic integrate-fire-reset neuron with stochasticity for neuromorphic computing
,” Nano Lett.
22
, 8437
–8444
(2022
).10.
S.
Yang
,
J.
Shin
,
T.
Kim
,
K. W.
Moon
,
J.
Kim
,
G.
Jang
,
D. S.
Hyeon
,
J.
Yang
,
C.
Hwang
,
Y. J.
Jeong
, and
J. P.
Hong
, “
Integrated neuromorphic computing networks by artificial spin synapses and spin neurons
,” NPG Asia Mater.
13
, 11
(2021
).11.
C.
Wang
,
C.
Lee
, and
K.
Roy
, “
Noise resilient leaky integrate-and-fire neurons based on multi-domain spintronic devices
,” Sci. Rep.
12
, 8361
(2022
).12.
D.
Wang
,
R.
Tang
,
H.
Lin
,
L.
Liu
,
N.
Xu
,
Y.
Sun
,
X.
Zhao
,
Z.
Wang
,
D.
Wang
,
Z.
Mai
,
Y.
Zhou
,
N.
Gao
,
C.
Song
,
L.
Zhu
,
T.
Wu
,
M.
Liu
, and
G.
Xing
, “
Spintronic leaky-integrate-fire spiking neurons with self-reset and winner-takes-all for neuromorphic computing
,” Nat. Commun.
14
, 1068
(2023
).13.
A.
Sengupta
,
P.
Panda
,
P.
Wijesinghe
,
Y.
Kim
, and
K.
Roy
, “
Magnetic tunnel junction mimics stochastic cortical spiking neurons
,” Sci. Rep.
6
, 30039
(2016
).14.
P.
Jadaun
,
C.
Cui
,
S.
Liu
, and
J. A. C.
Incorvia
, “
Adaptive cognition implemented with a context-aware and flexible neuron for next-generation artificial intelligence
,” PNAS Nexus
1
, pgac206
(2022
).15.
M. A.
Azam
,
D.
Bhattacharya
,
D.
Querlioz
, and
J.
Atulasimha
, “
Resonate and fire neuron with fixed magnetic skyrmions
,” J. Appl. Phys.
124
, 152122
(2018
).16.
H.
Vakili
,
J.-W.
Xu
,
W.
Zhou
,
M. N.
Sakib
,
M. G.
Morshed
,
T.
Hartnett
,
Y.
Quessab
,
K.
Litzius
,
C. T.
Ma
,
S.
Ganguly
,
M. R.
Stan
,
P. V.
Balachandran
,
G. S. D.
Beach
,
S. J.
Poon
,
A. D.
Kent
, and
A. W.
Ghosh
, “
Skyrmionics—Computing and memory technologies based on topological excitations in magnets
,” J. Appl. Phys.
130
, 070908
(2021
).17.
S.
Liu
,
T. P.
Xiao
,
C.
Cui
,
J. A. C.
Incorvia
,
C. H.
Bennett
, and
M. J.
Marinella
, “
A domain wall-magnetic tunnel junction artificial synapse with notched geometry for accurate and efficient training of deep neural networks
,” Appl. Phys. Lett.
118
, 202405
(2021
).18.
M.
Alamdar
,
T.
Leonard
,
C.
Cui
,
B. P.
Rimal
,
L.
Xue
,
O. G.
Akinola
,
T.
Patrick Xiao
,
J. S.
Friedman
,
C. H.
Bennett
,
M. J.
Marinella
, and
J. A. C.
Incorvia
, “
Domain wall-magnetic tunnel junction spin–orbit torque devices and circuits for in-memory computing
,” Appl. Phys. Lett.
118
, 112401
(2021
).19.
W. H.
Brigner
,
X.
Hu
,
N.
Hassan
,
C. H.
Bennett
,
J. A. C.
Incorvia
,
F.
Garcia-Sanchez
, and
J. S.
Friedman
, “
Graded-anisotropy-induced magnetic domain wall drift for an artificial spintronic leaky integrate-and-fire neuron
,” IEEE J. Explor. Solid-State Comput. Devices Circuits
5
, 19
–24
(2019
).20.
W. H.
Brigner
,
N.
Hassan
,
X.
Hu
,
C. H.
Bennett
,
F.
Garcia-Sanchez
,
C.
Cui
,
A.
Velasquez
,
M. J.
Marinella
,
J. A. C.
Incorvia
, and
J. S.
Friedman
, “
Domain wall leaky integrate-and-fire neurons with shape-based configurable activation functions
,” IEEE Trans. Electron Devices
69
, 2353
–2359
(2022
).21.
N.
Hassan
,
X.
Hu
,
L.
Jiang-Wei
,
W. H.
Brigner
,
O. G.
Akinola
,
F.
Garcia-Sanchez
,
M.
Pasquale
,
C. H.
Bennett
,
J. A. C.
Incorvia
, and
J. S.
Friedman
, “
Magnetic domain wall neuron with lateral inhibition
,” J. Appl. Phys.
124
, 152127
(2018
).22.
C.
Cui
,
O. G.
Akinola
,
N.
Hassan
,
C. H.
Bennett
,
M. J.
Marinella
,
J. S.
Friedman
, and
J. A. C.
Incorvia
, “
Maximized lateral inhibition in paired magnetic domain wall racetracks for neuromorphic computing
,” Nanotechnology
31
, 294001
(2020
).23.
S.
Liu
,
C.
Bennett
,
J.
Friedman
,
M.
Marinella
,
D.
Paydarfar
, and
J. A.
Incorvia
, “
Controllable reset behavior in domain wall–magnetic tunnel junction artificial neurons for task-adaptable computation
,” IEEE Magn. Lett.
12
, 20943805
(2021
).24.
P. O.
Mathews
,
C. B.
Duffee
,
A.
Thayil
,
T. E.
Stovall
,
C. H.
Bennett
,
F.
Garcia-Sanchez
,
M. J.
Marinella
,
J. A. C.
Incorvia
,
N.
Hassan
,
X.
Hu
, and
J. S.
Friedman
, “
High-speed CMOS-free purely spintronic asynchronous recurrent neural network
,” APL Mach. Learn.
1
, 016107
(2023
).25.
A.
Sengupta
,
Y.
Shim
, and
K.
Roy
, “
Proposal for an all-spin artificial neural network: Emulating neural and synaptic functionalities through domain wall motion in ferromagnets
,” IEEE Trans. Biomed. Circuits Syst.
10
, 1152
–1160
(2016
).26.
C.
Pehle
and
J. E.
Pedersen
(2021
). “Norse—A deep learning library for spiking neural networks” Zenodo
. https://doi.org/10.5281/zenodo.442202527.
D. E.
Nikonov
and
I. A.
Young
, “
Uniform methodology for benchmarking beyond-CMOS logic devices
,” in International Electron Devices Meeting
(
IEEE
, 2012
).28.
K.
Garello
,
F.
Yasin
,
H.
Hody
,
S.
Couet
,
L.
Souriau
,
S. H.
Sharifi
,
J.
Swerts
,
R.
Carpenter
,
S.
Rao
,
W.
Kim
,
J.
Wu
,
K. K. V.
Sethu
,
M.
Pak
,
N.
Jossart
,
D.
Crotti
,
A.
Furnemont
, and
G. S.
Kar
, “
Manufacturable 300 mm platform solution for field-free switching SOT-MRAM
,” in Symposium on VLSI Circuits
(
IEEE
, 2019
).29.
C.
Frenkel
,
M.
Lefebvre
,
J.-D.
Legat
, and
D.
Bol
, “
A 0.086-mm2 12.7-pJ/SOP 64k-synapse 256-neuron online-learning digital spiking neuromorphic processor in 28 nm CMOS
,” IEEE Trans. Biomed. Circuits Syst.
13
, 18413831
(2018
).30.
M.
Davies
,
N.
Srinivasa
,
T.-H.
Lin
,
G.
Chinya
,
Y.
Cao
,
S. H.
Choday
,
G.
Dimou
,
P.
Joshi
,
N.
Imam
,
S.
Jain
,
Y.
Liao
,
C.-K.
Lin
,
A.
Lines
,
R.
Liu
,
D.
Mathaikutty
,
S.
McCoy
,
A.
Paul
,
J.
Tse
,
G.
Venkataramanan
et al, “
Loihi: A neuromorphic manycore processor with on-chip learning
,” IEEE Micro
38
, 82
–99
(2018
).31.
G. S. D.
Beach
,
M.
Tsoi
, and
J. L.
Erskine
, “
Current-induced domain wall motion
,” J. Magn. Magn. Mater.
320
, 1272
–1281
(2008
).32.
K.-S.
Ryu
,
L.
Thomas
,
S.-H.
Yang
, and
S.
Parkin
, “
Chiral spin torque at magnetic domain walls
,” Nat. Nanotechnol.
8
, 527
–533
(2013
).33.
H.
Xiao
,
K.
Rasul
, and
R.
Vollgraf
, “
Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms
,” arXiv:1708.07747 (2017
).34.
35.
H.
Song
,
M.
Kim
,
D.
Park
,
Y.
Shin
, and
J.-G.
Lee
, “
Learning from noisy labels with deep neural networks: A survey
,” IEEE Trans. Neural Networks Learn. Syst.
2022
, 1
–19
.© 2023 Author(s). Published under an exclusive license by AIP Publishing.
2023
Author(s)
You do not currently have access to this content.