IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-51221-z.html
   My bibliography  Save this article

Fast and robust analog in-memory deep neural network training

Author

Listed:
  • Malte J. Rasch

    (TJ Watson Research Center
    Sony AI)

  • Fabio Carta

    (TJ Watson Research Center)

  • Omobayode Fagbohungbe

    (TJ Watson Research Center)

  • Tayfun Gokmen

    (TJ Watson Research Center)

Abstract

Analog in-memory computing is a promising future technology for efficiently accelerating deep learning networks. While using in-memory computing to accelerate the inference phase has been studied extensively, accelerating the training phase has received less attention, despite its arguably much larger compute demand to accelerate. While some analog in-memory training algorithms have been suggested, they either invoke significant amount of auxiliary digital compute—accumulating the gradient in digital floating point precision, limiting the potential speed-up—or suffer from the need for near perfectly programming reference conductance values to establish an algorithmic zero point. Here, we propose two improved algorithms for in-memory training, that retain the same fast runtime complexity while resolving the requirement of a precise zero point. We further investigate the limits of the algorithms in terms of conductance noise, symmetry, retention, and endurance which narrow down possible device material choices adequate for fast and robust in-memory deep neural network training.

Suggested Citation

  • Malte J. Rasch & Fabio Carta & Omobayode Fagbohungbe & Tayfun Gokmen, 2024. "Fast and robust analog in-memory deep neural network training," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51221-z
    DOI: 10.1038/s41467-024-51221-z
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-51221-z
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-51221-z?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. Weier Wan & Rajkumar Kubendran & Clemens Schaefer & Sukru Burc Eryilmaz & Wenqiang Zhang & Dabin Wu & Stephen Deiss & Priyanka Raina & He Qian & Bin Gao & Siddharth Joshi & Huaqiang Wu & H.-S. Philip , 2022. "A compute-in-memory chip based on resistive random-access memory," Nature, Nature, vol. 608(7923), pages 504-512, August.
    2. S. Ambrogio & P. Narayanan & A. Okazaki & A. Fasoli & C. Mackin & K. Hosokawa & A. Nomura & T. Yasuda & A. Chen & A. Friz & M. Ishii & J. Luquin & Y. Kohda & N. Saulnier & K. Brew & S. Choi & I. Ok & , 2023. "An analog-AI chip for energy-efficient speech recognition and transcription," Nature, Nature, vol. 620(7975), pages 768-775, August.
    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. Malte J. Rasch & Charles Mackin & Manuel Gallo & An Chen & Andrea Fasoli & Frédéric Odermatt & Ning Li & S. R. Nandakumar & Pritish Narayanan & Hsinyu Tsai & Geoffrey W. Burr & Abu Sebastian & Vijay N, 2023. "Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators," Nature Communications, Nature, vol. 14(1), pages 1-18, 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. Thomas Dalgaty & Filippo Moro & Yiğit Demirağ & Alessio Pra & Giacomo Indiveri & Elisa Vianello & Melika Payvand, 2024. "Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. Peng Chen & Fenghao Liu & Peng Lin & Peihong Li & Yu Xiao & Bihua Zhang & Gang Pan, 2023. "Open-loop analog programmable electrochemical memory array," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    3. 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.
    4. Simone D’Agostino & Filippo Moro & Tristan Torchet & Yiğit Demirağ & Laurent Grenouillet & Niccolò Castellani & Giacomo Indiveri & Elisa Vianello & Melika Payvand, 2024. "DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    5. Long Liu & Di Wang & Dandan Wang & Yan Sun & Huai Lin & Xiliang Gong & Yifan Zhang & Ruifeng Tang & Zhihong Mai & Zhipeng Hou & Yumeng Yang & Peng Li & Lan Wang & Qing Luo & Ling Li & Guozhong Xing & , 2024. "Domain wall magnetic tunnel junction-based artificial synapses and neurons for all-spin neuromorphic hardware," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    6. Yongxiang Li & Shiqing Wang & Ke Yang & Yuchao Yang & Zhong Sun, 2024. "An emergent attractor network in a passive resistive switching circuit," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    7. 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.
    8. Fadi Jebali & Atreya Majumdar & Clément Turck & Kamel-Eddine Harabi & Mathieu-Coumba Faye & Eloi Muhr & Jean-Pierre Walder & Oleksandr Bilousov & Amadéo Michaud & Elisa Vianello & Tifenn Hirtzlin & Fr, 2024. "Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    9. Yijun Li & Jianshi Tang & Bin Gao & Jian Yao & Anjunyi Fan & Bonan Yan & Yuchao Yang & Yue Xi & Yuankun Li & Jiaming Li & Wen Sun & Yiwei Du & Zhengwu Liu & Qingtian Zhang & Song Qiu & Qingwen Li & He, 2023. "Monolithic three-dimensional integration of RRAM-based hybrid memory architecture for one-shot learning," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    10. Mingrui Jiang & Keyi Shan & Chengping He & Can Li, 2023. "Efficient combinatorial optimization by quantum-inspired parallel annealing in analogue memristor crossbar," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    11. Malte J. Rasch & Charles Mackin & Manuel Gallo & An Chen & Andrea Fasoli & Frédéric Odermatt & Ning Li & S. R. Nandakumar & Pritish Narayanan & Hsinyu Tsai & Geoffrey W. Burr & Abu Sebastian & Vijay N, 2023. "Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    12. Yuyan Zhu & Yang Wang & Xingchen Pang & Yongbo Jiang & Xiaoxian Liu & Qing Li & Zhen Wang & Chunsen Liu & Weida Hu & Peng Zhou, 2024. "Non-volatile 2D MoS2/black phosphorus heterojunction photodiodes in the near- to mid-infrared region," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    13. Min, Fuhong & Zhang, Wen & Ji, Ziyi & Zhang, Lei, 2021. "Switching dynamics of a non-autonomous FitzHugh-Nagumo circuit with piecewise-linear flux-controlled memristor," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    14. 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.
    15. 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.
    16. Ying Zhang & Ge-Qi Mao & Xiaolong Zhao & Yu Li & Meiyun Zhang & Zuheng Wu & Wei Wu & Huajun Sun & Yizhong Guo & Lihua Wang & Xumeng Zhang & Qi Liu & Hangbing Lv & Kan-Hao Xue & Guangwei Xu & Xiangshui, 2021. "Evolution of the conductive filament system in HfO2-based memristors observed by direct atomic-scale imaging," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    17. Jens E. Pedersen & Steven Abreu & Matthias Jobst & Gregor Lenz & Vittorio Fra & Felix Christian Bauer & Dylan Richard Muir & Peng Zhou & Bernhard Vogginger & Kade Heckel & Gianvito Urgese & Sadasivan , 2024. "Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    18. Yueyang Jia & Qianqian Yang & Yue-Wen Fang & Yue Lu & Maosong Xie & Jianyong Wei & Jianjun Tian & Linxing Zhang & Rui Yang, 2024. "Giant tunnelling electroresistance in atomic-scale ferroelectric tunnel junctions," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    19. Xingan Jiang & Xueyun Wang & Xiaolei Wang & Xiangping Zhang & Ruirui Niu & Jianming Deng & Sheng Xu & Yingzhuo Lun & Yanyu Liu & Tianlong Xia & Jianming Lu & Jiawang Hong, 2022. "Manipulation of current rectification in van der Waals ferroionic CuInP2S6," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    20. Yu, Fei & Kong, Xinxin & Yao, Wei & Zhang, Jin & Cai, Shuo & Lin, Hairong & Jin, Jie, 2024. "Dynamics analysis, synchronization and FPGA implementation of multiscroll Hopfield neural networks with non-polynomial memristor," Chaos, Solitons & Fractals, Elsevier, vol. 179(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:15:y:2024:i:1:d:10.1038_s41467-024-51221-z. 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.