Please use this identifier to cite or link to this item: http://dspace.iitrpr.ac.in:8080/xmlui/handle/123456789/4520
Full metadata record
DC FieldValueLanguage
dc.contributor.authorVadde, V.-
dc.contributor.authorMuralidharan, B.-
dc.contributor.authorSharma, A.-
dc.date.accessioned2024-05-20T13:00:27Z-
dc.date.available2024-05-20T13:00:27Z-
dc.date.issued2024-05-20-
dc.identifier.urihttp://dspace.iitrpr.ac.in:8080/xmlui/handle/123456789/4520-
dc.description.abstractWe demonstrate that a magnetic tunnel junction injected with a spin Hall current can exhibit linear rotation of the magnetization of the free-ferromagnet using only the spin current. Using the linear resistance change of the magnetic tunnel junction (MTJ), we devise a circuit for the rectified linear activation (ReLU) function of the artificial neuron. We explore the role of different spin Hall effect (SHE) heavy metal (HM) layers on the power consumption of the ReLU circuit. We benchmark the power consumption of the ReLU circuit with different SHE layers by defining a new parameter called the spin Hall power factor. It combines the spin Hall angle, resistivity, and thickness of the HM layer, which translates to the power consumption of the different SHE layers during spin-orbit switching/rotation of the free FM. We employ a hybrid spintronics-CMOS simulation framework that couples Keldysh non-equilibrium Green’s function formalism with Landau–Lifshitz–Gilbert–Slonzewski equations and the HSPICE circuit simulator to account for the diverse physics of spin-transport and the CMOS elements in our proposed ReLU design. We also demonstrate the robustness of the proposed ReLU circuit against thermal noise and a non-trivial power-error trade-off that enables the use of an unstable free-ferromagnet for energy-efficient design. Using the proposed circuit, we evaluate the performance of the convolutional neural network for MNIST datasets and demonstrate comparable classification accuracies to the ideal ReLU with an energy consumption of 75 pJ per sample.en_US
dc.language.isoen_USen_US
dc.subjectCNN,en_US
dc.subjectReLuen_US
dc.subjectspintronicsen_US
dc.subjectspin Hall effecten_US
dc.titlePower efficient ReLU design for neuromorphic computing using spin Hall effecen_US
dc.typeArticleen_US
Appears in Collections:Year-2023

Files in This Item:
File Description SizeFormat 
full text.pdf1.48 MBAdobe PDFView/Open    Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.