Adapted MLP-Mixer network based on crossbar arrays of fast and multilevel switching (Co–Fe–B)<sub><i>x</i></sub>(LiNbO<sub>3</sub>)<sub>100−<i>x</i></sub> nanocomposite memristors
A. I. Iliasov, А. Н. Мацукатова, A. V. Emelyanov, Pavel Slepov, K. E. Nikiruy, V. V. Rylkov
Abstract
memristors. Firstly, we studied the characteristics of such memristors, including their minimal resistive switching time, which was extrapolated to be in the picosecond range. Secondly, we created a fully hardware NCS with memristive weights that are capable of classification of simple 4-bit vectors. The system was shown to be robust to noise introduction in the input patterns. Finally, we used experimental memristive characteristics to simulate an adapted MLP-Mixer architecture that demonstrated a classification accuracy of (94.7 ± 0.3)% on the Modified National Institute of Standards and Technology (MNIST) dataset. The obtained results are the first steps toward the realization of memristive NCS with a promising MLP-Mixer architecture.