Abstract:
We 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.
