Generative complex networks within a dynamic memristor with intrinsic variability

Yunpeng Guo, Wenrui Duan (), Xue Liu (), Xinxin Wang, Lidan Wang, Shukai Duan, Cheng Ma () and Huanglong Li ()
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Yunpeng Guo: Tsinghua University
Wenrui Duan: Beijing Information Science & Technology University
Xue Liu: Tsinghua University
Xinxin Wang: Tsinghua University
Lidan Wang: Southwest University
Shukai Duan: Southwest University
Cheng Ma: Tsinghua University
Huanglong Li: Tsinghua University

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: Abstract Artificial neural networks (ANNs) have gained considerable momentum in the past decade. Although at first the main task of the ANN paradigm was to tune the connection weights in fixed-architecture networks, there has recently been growing interest in evolving network architectures toward the goal of creating artificial general intelligence. Lagging behind this trend, current ANN hardware struggles for a balance between flexibility and efficiency but cannot achieve both. Here, we report on a novel approach for the on-demand generation of complex networks within a single memristor where multiple virtual nodes are created by time multiplexing and the non-trivial topological features, such as small-worldness, are generated by exploiting device dynamics with intrinsic cycle-to-cycle variability. When used for reservoir computing, memristive complex networks can achieve a noticeable increase in memory capacity a and respectable performance boost compared to conventional reservoirs trivially implemented as fully connected networks. This work expands the functionality of memristors for ANN computing.

Date: 2023
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DOI: 10.1038/s41467-023-41921-3

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