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

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, François Andrieu, Marc Bocquet, Stéphane Collin, Damien Querlioz () and Jean-Michel Portal ()
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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
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
Damien Querlioz: Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies
Jean-Michel Portal: Institut Matériaux Microélectronique Nanosciences de Provence

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-44766-6

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