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arXiv · 2106.16043

Reliability of Neural Networks Based on Spintronic Neurons

Eleonora Raimondo·Anna Giordano·Andrea Grimaldi·Vito Puliafito·Mario Carpentieri·Zhongming Zeng·Riccardo Tomasello·Giovanni Finocchio
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Abstract

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.

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Eleonora Raimondo, Anna Giordano, Andrea Grimaldi, Vito Puliafito, Mario Carpentieri, Zhongming Zeng, Riccardo Tomasello, Giovanni Finocchio. 2021-06-30. Reliability of Neural Networks Based on Spintronic Neurons. https://doi.org/10.1109/lmag.2021.3100317

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