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Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell

Author

Listed:
  • Fadi Jebali

    (Institut Matériaux Microélectronique Nanosciences de Provence)

  • Atreya Majumdar

    (Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies)

  • Clément Turck

    (Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies)

  • Kamel-Eddine Harabi

    (Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies)

  • Mathieu-Coumba Faye

    (Institut Matériaux Microélectronique Nanosciences de Provence
    Université Grenoble Alpes, CEA, LETI)

  • Eloi Muhr

    (Institut Matériaux Microélectronique Nanosciences de Provence)

  • Jean-Pierre Walder

    (Institut Matériaux Microélectronique Nanosciences de Provence)

  • Oleksandr Bilousov

    (Institut Photovoltaïque d’Ile-de-France (IPVF))

  • Amadéo Michaud

    (Institut Photovoltaïque d’Ile-de-France (IPVF))

  • Elisa Vianello

    (Université Grenoble Alpes, CEA, LETI)

  • Tifenn Hirtzlin

    (Université Grenoble Alpes, CEA, LETI)

  • François Andrieu

    (Université Grenoble Alpes, CEA, LETI)

  • Marc Bocquet

    (Institut Matériaux Microélectronique Nanosciences de Provence)

  • Stéphane Collin

    (Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies
    Institut Photovoltaïque d’Ile-de-France (IPVF))

  • Damien Querlioz

    (Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies)

  • Jean-Michel Portal

    (Institut Matériaux Microélectronique Nanosciences de Provence)

Abstract

Memristor-based neural networks provide an exceptional energy-efficient platform for artificial intelligence (AI), presenting the possibility of self-powered operation when paired with energy harvesters. However, most memristor-based networks rely on analog in-memory computing, necessitating a stable and precise power supply, which is incompatible with the inherently unstable and unreliable energy harvesters. In this work, we fabricated a robust binarized neural network comprising 32,768 memristors, powered by a miniature wide-bandgap solar cell optimized for edge applications. Our circuit employs a resilient digital near-memory computing approach, featuring complementarily programmed memristors and logic-in-sense-amplifier. This design eliminates the need for compensation or calibration, operating effectively under diverse conditions. Under high illumination, the circuit achieves inference performance comparable to that of a lab bench power supply. In low illumination scenarios, it remains functional with slightly reduced accuracy, seamlessly transitioning to an approximate computing mode. Through image classification neural network simulations, we demonstrate that misclassified images under low illumination are primarily difficult-to-classify cases. Our approach lays the groundwork for self-powered AI and the creation of intelligent sensors for various applications in health, safety, and environment monitoring.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-44766-6
    DOI: 10.1038/s41467-024-44766-6
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    1. Kilian D. Stenning & Jack C. Gartside & Luca Manneschi & Christopher T. S. Cheung & Tony Chen & Alex Vanstone & Jake Love & Holly Holder & Francesco Caravelli & Hidekazu Kurebayashi & Karin Everschor-, 2024. "Neuromorphic overparameterisation and few-shot learning in multilayer physical neural networks," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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