Memristor-based analogue computing for brain-inspired sound localization with in situ training

Bin Gao (), Ying Zhou, Qingtian Zhang, Shuanglin Zhang, Peng Yao, Yue Xi, Qi Liu, Meiran Zhao, Wenqiang Zhang, Zhengwu Liu, Xinyi Li, Jianshi Tang, He Qian and Huaqiang Wu ()
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Bin Gao: Tsinghua University
Ying Zhou: Tsinghua University
Qingtian Zhang: Tsinghua University
Shuanglin Zhang: Tsinghua University
Peng Yao: Tsinghua University
Yue Xi: Tsinghua University
Qi Liu: Tsinghua University
Meiran Zhao: Tsinghua University
Wenqiang Zhang: Tsinghua University
Zhengwu Liu: Tsinghua University
Xinyi Li: Tsinghua University
Jianshi Tang: Tsinghua University
He Qian: Tsinghua University
Huaqiang Wu: Tsinghua University

Nature Communications, 2022, vol. 13, issue 1, 1-8

Abstract: Abstract The human nervous system senses the physical world in an analogue but efficient way. As a crucial ability of the human brain, sound localization is a representative analogue computing task and often employed in virtual auditory systems. Different from well-demonstrated classification applications, all output neurons in localization tasks contribute to the predicted direction, introducing much higher challenges for hardware demonstration with memristor arrays. In this work, with the proposed multi-threshold-update scheme, we experimentally demonstrate the in-situ learning ability of the sound localization function in a 1K analogue memristor array. The experimental and evaluation results reveal that the scheme improves the training accuracy by ∼45.7% compared to the existing method and reduces the energy consumption by ∼184× relative to the previous work. This work represents a significant advance towards memristor-based auditory localization system with low energy consumption and high performance.

Date: 2022
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DOI: 10.1038/s41467-022-29712-8

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