Ferrimagnetic Synapse Devices for Fast and Energy-Efficient On-Chip Learning on Crossbar-Array-Based Neural Networks (A Device-Circuit-System Costudy)

We propose a ferrimagnetic domain-wall synapse device as an alternative to its ferromagnetic counterpart for faster and more energy-efficient on-chip learning on a crossbar-array-based analog-hardware neural network. We first use micromagnetics to model domain-wall motion in a Co-Gd-bilayer-based ferrimagnetic device. We show that at the angular-momentum-compensation temperature, the ferrimagnetic-domain-wall velocity in this device is about 2–2.5 times (
$22.5\times $
) higher than the ferromagnetic-domain-wall velocity in a CoFe/Pt device at room temperature, for the same value of current density. This velocity ratio is consistent with recent experimental findings. Next, we incorporate the results from our micromagnetic model in SPICE circuit simulation of a crossbar-array-based fully connected neural network (FCNN) using a Verilog-A model for each synapse in the crossbar array. We show that as a consequence of faster domain-wall motion in the ferrimagnetic device compared to the ferromagnetic device, for the same duration of synaptic-weight-modulating current pulses, total energy consumption in the synapses for on-chip learning is five times (
$5\times $
) lower in the ferrimagnetic-synapse-based FCNN compared to the ferromagnetic-synapse-based FCNN. Similarly, for the same amount of energy consumed for learning, time taken for on-chip learning is five times (
$5\times $
) lower for the former compared to the latter.