Reservoir computing using dynamic memristors for temporal information processing

Chao Du, Fuxi Cai, Mohammed A. Zidan, Wen Ma, Seung Hwan Lee and Wei D. Lu ()
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Chao Du: University of Michigan
Fuxi Cai: University of Michigan
Mohammed A. Zidan: University of Michigan
Wen Ma: University of Michigan
Seung Hwan Lee: University of Michigan
Wei D. Lu: University of Michigan

Nature Communications, 2017, vol. 8, issue 1, 1-10

Abstract: Abstract Reservoir computing systems utilize dynamic reservoirs having short-term memory to project features from the temporal inputs into a high-dimensional feature space. A readout function layer can then effectively analyze the projected features for tasks, such as classification and time-series analysis. The system can efficiently compute complex and temporal data with low-training cost, since only the readout function needs to be trained. Here we experimentally implement a reservoir computing system using a dynamic memristor array. We show that the internal ionic dynamic processes of memristors allow the memristor-based reservoir to directly process information in the temporal domain, and demonstrate that even a small hardware system with only 88 memristors can already be used for tasks, such as handwritten digit recognition. The system is also used to experimentally solve a second-order nonlinear task, and can successfully predict the expected output without knowing the form of the original dynamic transfer function.

Date: 2017
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DOI: 10.1038/s41467-017-02337-y

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