AMU-IM2NP, CEA Leti and Université Paris Saclay have collaborated to publish their results in Nature Communications on:
« 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 & Jean-Michel Portal. Nature Communications (2024)15:741. https://doi.org/10.1038/s41467-024-44766-6
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.