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Spatiotemporal Data Processing with Memristor Crossbar-Array-Based Graph Reservoir. | LitMetric

Spatiotemporal Data Processing with Memristor Crossbar-Array-Based Graph Reservoir.

Adv Mater

Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.

Published: February 2024

AI Article Synopsis

  • - Memristor-based physical reservoir computing (RC) struggles to effectively process complex data because traditional methods assign only one input to each memristor, which limits capturing spatial relationships.
  • - A new "graph reservoir" system is introduced, utilizing a metal cell in a diagonal-crossbar array (mCBA) with dynamic memristors to better store and represent correlations between input signals.
  • - This innovative approach yields impressive results, achieving a 0.09 error rate in time series prediction, 97.21% accuracy in recognizing handwritten digits (MNIST), and 80.0% accuracy in diagnosing human brain connectivity.

Article Abstract

Memristor-based physical reservoir computing (RC) is a robust framework for processing complex spatiotemporal data parallelly. However, conventional memristor-based reservoirs cannot capture the spatial relationship between the time-varying inputs due to the specific mapping scheme assigning one input signal to one memristor conductance. Here, a physical "graph reservoir" is introduced using a metal cell at the diagonal-crossbar array (mCBA) with dynamic self-rectifying memristors. Input and inverted input signals are applied to the word and bit lines of the mCBA, respectively, storing the correlation information between input signals in the memristors. In this way, the mCBA graph reservoirs can map the spatiotemporal correlation of the input data in a high-dimensional feature space. The high-dimensional mapping characteristics of the graph reservoir achieve notable results, including a normalized root-mean-square error of 0.09 in Mackey-Glass time series prediction, a 97.21% accuracy in MNIST recognition, and an 80.0% diagnostic accuracy in human connectome classification.

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Source
http://dx.doi.org/10.1002/adma.202309314DOI Listing

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