Neuromorphic hardware,as a non-Von Neumann architecture,has better energy efficiency and parallelism than the conventional computer.Here,with the numerical modeling spin-orbit torque(SOT)device using current-induced S...Neuromorphic hardware,as a non-Von Neumann architecture,has better energy efficiency and parallelism than the conventional computer.Here,with the numerical modeling spin-orbit torque(SOT)device using current-induced SOT and Joule heating effects,we acquire its magnetization stochastic switching probability as a function of the interval time of input current pulses and use it to mimic the spike-timing-dependent plasticity learning behavior like actual brain working.We further demonstrate that the artificial spiking neural network(SNN)built by this SOT device can perform unsupervised handwritten digit recognition with an accuracy of 80%and logic operation learning.Our work provides a new clue to achieving SNN-based neuromorphic hardware using high-energy efficiency and nonvolatile spintronics nanodevices.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.12074178)the Open Research Fund of Jiangsu Provincial Key Laboratory for Nanotechnology.
文摘Neuromorphic hardware,as a non-Von Neumann architecture,has better energy efficiency and parallelism than the conventional computer.Here,with the numerical modeling spin-orbit torque(SOT)device using current-induced SOT and Joule heating effects,we acquire its magnetization stochastic switching probability as a function of the interval time of input current pulses and use it to mimic the spike-timing-dependent plasticity learning behavior like actual brain working.We further demonstrate that the artificial spiking neural network(SNN)built by this SOT device can perform unsupervised handwritten digit recognition with an accuracy of 80%and logic operation learning.Our work provides a new clue to achieving SNN-based neuromorphic hardware using high-energy efficiency and nonvolatile spintronics nanodevices.
