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• W4311582372 abstract "Reservoir computing (RC) is a computational architecture capable of efficiently processing temporal information, which allows low-cost 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." @default.
• W4311582372 created "2022-12-27" @default.
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• W4311582372 date "2022-12-15" @default.
• W4311582372 modified "2023-10-06" @default.
• W4311582372 title "A Perovskite Memristor with Large Dynamic Space for Analog-Encoded Image Recognition" @default.
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• W4311582372 doi "https://doi.org/10.1021/acsnano.2c09569" @default.
• W4311582372 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36519795" @default.
• W4311582372 hasPublicationYear "2022" @default.
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