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Cellular automata imbedded memristor-based recirculated logic in-memory computing

Author

Listed:
  • Yanming Liu

    (Tsinghua University
    Tsinghua University)

  • He Tian

    (Tsinghua University
    Tsinghua University)

  • Fan Wu

    (Tsinghua University
    Tsinghua University)

  • Anhan Liu

    (Tsinghua University
    Tsinghua University)

  • Yihao Li

    (Tsinghua University)

  • Hao Sun

    (Tsinghua University
    Tsinghua University)

  • Mario Lanza

    (King Abdullah University of Science and Technology (KAUST))

  • Tian-Ling Ren

    (Tsinghua University
    Tsinghua University)

Abstract

Memristor-based circuits offer low hardware costs and in-memory computing, but full-memristive circuit integration for different algorithm remains limited. Cellular automata (CA) has been noticed for its well-known parallel, bio-inspired, computational characteristics. Running CA on conventional chips suffers from low parallelism and high hardware costs. Establishing dedicated hardware for CA remains elusive. We propose a recirculated logic operation scheme (RLOS) using memristive hardware and 2D transistors for CA evolution, significantly reducing hardware complexity. RLOS’s versatility supports multiple CA algorithms on a single circuit, including elementary CA rules and more complex majority classification and edge detection algorithms. Results demonstrate up to a 79-fold reduction in hardware costs compared to FPGA-based approaches. RLOS-based reservoir computing is proposed for edge computing development, boasting the lowest hardware cost (6 components/per cell) among existing implementations. This work advances efficient, low-cost CA hardware and encourages edge computing hardware exploration.

Suggested Citation

  • Yanming Liu & He Tian & Fan Wu & Anhan Liu & Yihao Li & Hao Sun & Mario Lanza & Tian-Ling Ren, 2023. "Cellular automata imbedded memristor-based recirculated logic in-memory computing," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38299-7
    DOI: 10.1038/s41467-023-38299-7
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    References listed on IDEAS

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    1. Chao Du & Fuxi Cai & Mohammed A. Zidan & Wen Ma & Seung Hwan Lee & Wei D. Lu, 2017. "Reservoir computing using dynamic memristors for temporal information processing," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    2. Anamarija Fofonjka & Michel C. Milinkovitch, 2021. "Reaction-diffusion in a growing 3D domain of skin scales generates a discrete cellular automaton," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
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