A Perovskite Memristor with Large Dynamic Space for Analog-Encoded Image Recognition

Reservoir computing (RC) is a computational architecture capable of efficiently processing temporal information, which allows lowcost hardware implementation. However, the previously reported memristor-based RC mostly utilized binarized data sets to reduce the difficulty of signal processing of the memristor, which inevitably induces data distortion to a certain extent, leading to poor network computing performance. Here, we report on a RC system in a fully memristive architecture based on solution-processed perovskite memristors. The perovskite memristor exhibits 10000 conductance states with a modulation range of more than 4 orders of magnitude. The obtained tens of thousands of finely spaced conductance states with a near-ideal analog property provide a sufficiently large dynamic range and enough intermediate states, which were further applied as a reservoir to map the feature information on different sequential inputs in an analog way. The computing capability of the image classification task of a Fashion-MNIST data set with a high recognition accuracy of up to 90.1% shows that the excellent analog and short-term properties of our perovskite memristor allow the hardware implementation of neuromorphic computing with a reduced training cost.

Automated summary

Reservoir computing is a computational architecture that efficiently processes temporal information and enables low-cost hardware implementation. This study presents a fully memristive RC system based on solution-processed perovskite memristors, which exhibit a large dynamic range and analog properties. The system achieves high recognition accuracy in image classification tasks, showcasing the potential of perovskite memristors for neuromorphic computing.