SHE-MTJ based ReLU-max pooling functions for on-chip training of neural networks
In: AIP Advances, Jg. 14 (2024), Heft 2, S. 025130-025130-6
Online
academicJournal
Zugriff:
We present a detailed investigation of various routes to optimize the power consumption of the spintronic-based devices for implementing rectified linear activation (ReLU) and max-pooling functions. We examine the influence of various spin Hall effect layers, and their input resistances on the power consumption of the ReLU-max pooling functions, we also access the impact of the thermal stability factor of the free-ferromagnet layer on the power consumption and accuracy of the device. The design for ReLU-max pooling relies on the continuous rotation of magnetization, which is accomplished by applying orthogonal spin current to the free-FM layer. We also demonstrate the non-trivial power-resistance relation, where the power consumption decreases with an increase in SHE resistance. We utilize the hybrid spintronic-CMOS simulation platform that combines Keldysh non-equilibrium Green’s function (NEGF) with Landau-Lifshitz-Gilbert-Slonzewski (LLGS) equations and the HSPICE circuit simulator to evaluate our network. Our design takes 0.343 μW of power for ReLU emulation and 17.86 μW of power for ReLU-max pooling network implementation at a thermal stability factor of 4.58, all while maintaining reliable results. We validate the efficiency of our design by implementing a convolutional neural network that classifies the handwritten-MNIST and fashion-MNIST datasets. This implementation illustrates that the classification accuracies achieved are on par with those attained using the ideal software ReLU-max pooling functions, with an energy consumption of 167.31 pJ per sample.
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SHE-MTJ based ReLU-max pooling functions for on-chip training of neural networks
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Autor/in / Beteiligte Person: | Vadde, Venkatesh ; Muralidharan, Bhaskaran ; Sharma, Abhishek |
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Zeitschrift: | AIP Advances, Jg. 14 (2024), Heft 2, S. 025130-025130-6 |
Veröffentlichung: | AIP Publishing LLC, 2024 |
Medientyp: | academicJournal |
ISSN: | 2158-3226 (print) ; 0712-5445 (print) |
DOI: | 10.1063/9.0000685 |
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