Multilayer magnetic skyrmion devices for spiking neural networks - Aijaz H Lone; Daniel N Rahimi; Hossein Fariborzi; Gianluca Setti (Neuromorphic Computing and Engineering)
Spintronic devices -based on magnetic solitons, such as the domain wall motion and the skyrmions, have shown a significant potential for applications in energy-efficient data storage and beyond CMOS computing architectures. Based on the magnetic multilayer hetero-structures, we propose a magnetic skyrmion-magnetic tunnel junction device structure, mimicking leaky integrate and fire LIF neuron characteristics. The device is controlled by spin-orbit torque-SOT driven skyrmion motion in the ferromagnetic thin film. The modified leaky integrate and fire LIF neuron-like characteristics are shown using the combination of SOT and the skyrmion position dependence of the demagnetization energy. The device characteristics are modeled as the modified LIF neuron. The LIF neuron is one of the fundamental spiking neuron models; we integrate the model in the three-layer spiking neural network (SNN) and convolutional CSNN framework to test these spiking neuron models to classify the MNIST and FMNIST datasets. In both architectures, the network achieves classification accuracy above 97.10%. Additionally, the LIF neuron latency is in ns; thus, when integrated with the CMOS, the proposed device structures and associated systems exhibit an excellent future for energy-efficient neuromorphic computing.