Spin Orbit Torque Based Electronic Neuron
Abhronil Sengupta, Sri Harsha Choday, Yusung Kim, and Kaushik Roy

TL;DR
This paper proposes a spin-orbit torque based electronic neuron that mimics neural thresholding, demonstrating lower power consumption and high accuracy in image classification tasks compared to traditional CMOS neural networks.
Contribution
It introduces a novel SOT-based device functioning as an artificial neuron with a two-step switching scheme for thresholding.
Findings
Consumes ~3X less power than 45nm CMOS neural networks
Achieves ~80% accuracy on MNIST digit classification
Demonstrates feasibility of spintronic neurons for neural network applications
Abstract
A device based on current-induced spin-orbit torque (SOT) that functions as an electronic neuron is proposed in this work. The SOT device implements an artificial neuron's thresholding (transfer) function. In the first step of a two-step switching scheme, a charge current places the magnetization of a nano-magnet along the hard-axis i.e. an unstable point for the magnet. In the second step, the SOT device (neuron) receives a current (from the synapses) which moves the magnetization from the unstable point to one of the two stable states. The polarity of the synaptic current encodes the excitatory and inhibitory nature of the neuron input, and determines the final orientation of the magnetization. A resistive crossbar array, functioning as synapses, generates a bipolar current that is a weighted sum of the inputs. The simulation of a two layer feed-forward Artificial Neural Network (ANN)…
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