Ultrafast and Energy-Efficient Ferrimagnetic XNOR Logic Gates for Binary Neural Networks

摘要

Ultrafast current-driven domain wall (DW) motions have been realized in ferrimagnetic (FiM) nanowires. However, the FiM dynamics can be significantly affected by the Joule-heating. In this work, we propose a highly efficient XNOR logic gate by properly leveraging the thermal effect on the FiM DW motions. Its functionality and advantageous performance have been confirmed by the micromagnetic simulations. Moreover, majority logic and full adder functions can also be reconfigured based on the proposed scheme. Lastly, a fully FiM DW based binary neural network (BNN) is built, which provides low energy consumption, short delay and excellent accuracy.