IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-38299-7.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-38299-7
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-38299-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    3. Peng Yao & Huaqiang Wu & Bin Gao & Jianshi Tang & Qingtian Zhang & Wenqiang Zhang & J. Joshua Yang & He Qian, 2020. "Fully hardware-implemented memristor convolutional neural network," Nature, Nature, vol. 577(7792), pages 641-646, January.
    4. Yanan Zhong & Jianshi Tang & Xinyi Li & Bin Gao & He Qian & Huaqiang Wu, 2021. "Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    5. Zhengwu Liu & Jianshi Tang & Bin Gao & Peng Yao & Xinyi Li & Dingkun Liu & Ying Zhou & He Qian & Bo Hong & Huaqiang Wu, 2020. "Neural signal analysis with memristor arrays towards high-efficiency brain–machine interfaces," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiangpeng Liang & Yanan Zhong & Jianshi Tang & Zhengwu Liu & Peng Yao & Keyang Sun & Qingtian Zhang & Bin Gao & Hadi Heidari & He Qian & Huaqiang Wu, 2022. "Rotating neurons for all-analog implementation of cyclic reservoir computing," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Zhongfang Zhang & Xiaolong Zhao & Xumeng Zhang & Xiaohu Hou & Xiaolan Ma & Shuangzhu Tang & Ying Zhang & Guangwei Xu & Qi Liu & Shibing Long, 2022. "In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    3. Jangsaeng Kim & Eun Chan Park & Wonjun Shin & Ryun-Han Koo & Chang-Hyeon Han & He Young Kang & Tae Gyu Yang & Youngin Goh & Kilho Lee & Daewon Ha & Suraj S. Cheema & Jae Kyeong Jeong & Daewoong Kwon, 2024. "Analog reservoir computing via ferroelectric mixed phase boundary transistors," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    4. Djohan Bonnet & Tifenn Hirtzlin & Atreya Majumdar & Thomas Dalgaty & Eduardo Esmanhotto & Valentina Meli & Niccolo Castellani & Simon Martin & Jean-François Nodin & Guillaume Bourgeois & Jean-Michel P, 2023. "Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    5. Zhiwei Chen & Wenjie Li & Zhen Fan & Shuai Dong & Yihong Chen & Minghui Qin & Min Zeng & Xubing Lu & Guofu Zhou & Xingsen Gao & Jun-Ming Liu, 2023. "All-ferroelectric implementation of reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    6. Yulin Feng & Yizhou Zhang & Zheng Zhou & Peng Huang & Lifeng Liu & Xiaoyan Liu & Jinfeng Kang, 2024. "Memristor-based storage system with convolutional autoencoder-based image compression network," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    7. 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 & Huaqiang Wu, 2022. "Memristor-based analogue computing for brain-inspired sound localization with in situ training," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    8. Zhuohui Liu & Qinghua Zhang & Donggang Xie & Mingzhen Zhang & Xinyan Li & Hai Zhong & Ge Li & Meng He & Dashan Shang & Can Wang & Lin Gu & Guozhen Yang & Kuijuan Jin & Chen Ge, 2023. "Interface-type tunable oxygen ion dynamics for physical reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    9. Ruomin Zhu & Sam Lilak & Alon Loeffler & Joseph Lizier & Adam Stieg & James Gimzewski & Zdenka Kuncic, 2023. "Online dynamical learning and sequence memory with neuromorphic nanowire networks," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    10. See-On Park & Hakcheon Jeong & Jongyong Park & Jongmin Bae & Shinhyun Choi, 2022. "Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    11. Sanghyeon Choi & Jaeho Shin & Gwanyeong Park & Jung Sun Eo & Jingon Jang & J. Joshua Yang & Gunuk Wang, 2024. "3D-integrated multilayered physical reservoir array for learning and forecasting time-series information," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    12. Han Zhao & Zhengwu Liu & Jianshi Tang & Bin Gao & Qi Qin & Jiaming Li & Ying Zhou & Peng Yao & Yue Xi & Yudeng Lin & He Qian & Huaqiang Wu, 2023. "Energy-efficient high-fidelity image reconstruction with memristor arrays for medical diagnosis," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    13. Zhang, Shaohua & Zhang, Hongli & Wang, Cong & Lin, Hairong, 2024. "Bionic modeling and dynamics analysis of heterogeneous brain regions connected by memristive synaptic crosstalk," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
    14. Yiming Sun & Tao Lin & Na Lei & Xing Chen & Wang Kang & Zhiyuan Zhao & Dahai Wei & Chao Chen & Simin Pang & Linglong Hu & Liu Yang & Enxuan Dong & Li Zhao & Lei Liu & Zhe Yuan & Aladin Ullrich & Chris, 2023. "Experimental demonstration of a skyrmion-enhanced strain-mediated physical reservoir computing system," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    15. Choi, Woo Sik & Kim, Donguk & Yang, Tae Jun & Chae, Inseok & Kim, Changwook & Kim, Hyungjin & Kim, Dae Hwan, 2022. "Electrode-dependent electrical switching characteristics of InGaZnO memristor," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    16. Changsong Gao & Di Liu & Chenhui Xu & Weidong Xie & Xianghong Zhang & Junhua Bai & Zhixian Lin & Cheng Zhang & Yuanyuan Hu & Tailiang Guo & Huipeng Chen, 2024. "Toward grouped-reservoir computing: organic neuromorphic vertical transistor with distributed reservoir states for efficient recognition and prediction," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    17. Min Yan & Can Huang & Peter Bienstman & Peter Tino & Wei Lin & Jie Sun, 2024. "Emerging opportunities and challenges for the future of reservoir computing," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    18. Ali Momeni & Romain Fleury, 2022. "Electromagnetic wave-based extreme deep learning with nonlinear time-Floquet entanglement," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    19. Shuzhi Liu & Jianmin Zeng & Zhixin Wu & Han Hu & Ao Xu & Xiaohe Huang & Weilin Chen & Qilai Chen & Zhe Yu & Yinyu Zhao & Rong Wang & Tingting Han & Chao Li & Pingqi Gao & Hyunwoo Kim & Seung Jae Baik , 2023. "An ultrasmall organic synapse for neuromorphic computing," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    20. Lee, Geun Ho & Kim, Tae-Hyeon & Song, Min Suk & Park, Jinwoo & Kim, Sungjoon & Hong, Kyungho & Kim, Yoon & Park, Byung-Gook & Kim, Hyungjin, 2022. "Effect of weight overlap region on neuromorphic system with memristive synaptic devices," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38299-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.