Reliability of Neural Networks Based on Spintronic Neurons

Spintronic technology is emerging as a direction for the hardware implementation of neurons and synapses of neuromorphic architectures. In particular, a single spintronic device can be used to implement the nonlinear activation function of neurons. Here, we propose how to implement spintronic neuron...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Raimondo, Eleonora, Giordano, Anna, Grimaldi, Andrea, Puliafito, Vito, Carpentieri, Mario, Zeng, Zhongming, Tomasello, Riccardo, Finocchio, Giovanni
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 05.01.2022
Subjects
Hardware
Mathematical analysis
Neural networks
Neurons
Physics - Mesoscale and Nanoscale Physics
Robustness (mathematics)
Synapses
Online AccessGet full text

Cover

More Information
Summary:Spintronic technology is emerging as a direction for the hardware implementation of neurons and synapses of neuromorphic architectures. In particular, a single spintronic device can be used to implement the nonlinear activation function of neurons. Here, we propose how to implement spintronic neurons with a sigmoidal and ReLU-like activation functions. We then perform a numerical experiment showing the robustness of neural networks made by spintronic neurons all having different activation functions to emulate device-to-device variations in a possible hardware implementation of the network. Therefore, we consider a vanilla neural network implemented to recognize the categories of the Mixed National Institute of Standards and Technology database, and we show an average accuracy of 98.87 % in the test dataset which is very close to the 98.89% as obtained for the ideal case (all neurons have the same sigmoid activation function). Similar results are also obtained with neurons having a ReLU-like activation function.
ISSN:2331-8422
DOI:10.48550/arxiv.2106.16043