Time-varying data processing with nonvolatile memristor-based temporal kernel

Yoon Ho Jang, Woohyun Kim, Jihun Kim, Kyung Seok Woo, Hyun Jae Lee, Jeong Woo Jeon, Sung Keun Shim, Janguk Han and Cheol Seong Hwang ()
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Yoon Ho Jang: Seoul National University
Woohyun Kim: Seoul National University
Jihun Kim: Seoul National University
Kyung Seok Woo: Seoul National University
Hyun Jae Lee: Seoul National University
Jeong Woo Jeon: Seoul National University
Sung Keun Shim: Seoul National University
Janguk Han: Seoul National University
Cheol Seong Hwang: Seoul National University

Nature Communications, 2021, vol. 12, issue 1, 1-9

Abstract: Abstract Recent advances in physical reservoir computing, which is a type of temporal kernel, have made it possible to perform complicated timing-related tasks using a linear classifier. However, the fixed reservoir dynamics in previous studies have limited application fields. In this study, temporal kernel computing was implemented with a physical kernel that consisted of a W/HfO2/TiN memristor, a capacitor, and a resistor, in which the kernel dynamics could be arbitrarily controlled by changing the circuit parameters. After the capability of the temporal kernel to identify the static MNIST data was proven, the system was adopted to recognize the sequential data, ultrasound (malignancy of lesions) and electrocardiogram (arrhythmia), that had a significantly different time constant (10−7 vs. 1 s). The suggested system feasibly performed the tasks by simply varying the capacitance and resistance. These functionalities demonstrate the high adaptability of the present temporal kernel compared to the previous ones.

Date: 2021
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DOI: 10.1038/s41467-021-25925-5

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