Multilayer ferromagnetic spintronic devices for neuromorphic computing applications

Lone, Aijaz H.; Zou, Xuecui; Mishra, Kishan K.; Singaravelu, Venkatesh; Sbiaa, R.; Fariborzi, Hossein; Setti, Gianluca

doi:10.1039/d4nr01003e

Nanoscale

2024

DOI: 10.1039/d4nr01003e

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Multilayer ferromagnetic spintronic devices for neuromorphic computing applications

Aijaz H. Lone,

Xuecui Zou,

Kishan K. Mishra

et al.

Abstract: Based on ferromagnetic thin film systems, Spintronic devices show substantial prospects for energy-efficient memory, logic, and unconventional computing paradigms. This paper presents a multilayer ferromagnetic spintronic device's experimental and micromagnetic...

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Ferrimagnet-Based Neuromorphic Device Mimicking the Ventral Visual Pathway for High-Accuracy Target Recognition

Zeng,

Chen,

Liu

et al. 2024

ACS Appl. Mater. Interfaces

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The ventral visual pathway (VVP) of the human brain efficiently implements target recognition by employing a deep hierarchical structure to build complex visual concepts from simple features. Artificial neural networks (ANNs) based on spintronic devices are capable of target recognition, but their poor interpretability and limited network depth hinder ANNs from mimicking the VVP. Hardware implementation of the VVP requires a biorealistic spintronic device as well as the corresponding interpretable and deep network structure, which have not been reported so far. Here, we report a ferrimagnetic neuron with a continuously differentiable exponential linear unit (CeLu) activation function, which is closer to biological neurons and could mitigate the issue of limited network depth. Meanwhile, we also demonstrate that a ferrimagnet can construct artificial synapses with high linearity and symmetry to meet the requirements of weight update algorithms. Based on these neurons and synapses, we propose an all-spin convolutional neural network (CNN) with a high interpretability and deep neural network, to mimic the VVP. Compared to the state-of-the-art spintronic-based neuromorphic computing model, the CNN with bionic function, using experimentally derived device parameters, achieves high recognition accuracies of over 91% and 98% on the CIFAR-10 datasets and the MNIST datasets, respectively, showing improvements of 1.13% and 1.76%. Our work provides a promising method to improve the bionic performance of spintronic device-based neural networks.

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Ferrimagnet-Based Neuromorphic Device Mimicking the Ventral Visual Pathway for High-Accuracy Target Recognition

Zeng,

Chen,

Liu

et al. 2024

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

show abstract

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Multilayer ferromagnetic spintronic devices for neuromorphic computing applications

Abstract: Based on ferromagnetic thin film systems, Spintronic devices show substantial prospects for energy-efficient memory, logic, and unconventional computing paradigms. This paper presents a multilayer ferromagnetic spintronic device's experimental and micromagnetic...

Cited by 1 publication

References 40 publications

Ferrimagnet-Based Neuromorphic Device Mimicking the Ventral Visual Pathway for High-Accuracy Target Recognition

Ferrimagnet-Based Neuromorphic Device Mimicking the Ventral Visual Pathway for High-Accuracy Target Recognition

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