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

Memristor-based neural networks offer energy-efficient AI, potentially self-powered with energy harvesters. However, most memristor networks rely on analog in-memory computing, needing stable power incompatible with unstable energy harvesters. This work details a robust binarized neural network (32,768 memristors) powered by a miniature wide-bandgap solar cell. Using digital near-memory computing with complementary memristors and logic-in-sense-amplifier, the circuit avoids compensation/calibration, functioning under diverse conditions. High illumination yields performance comparable to a lab power supply; low illumination maintains functionality with slightly reduced accuracy, transitioning to approximate computing. Image classification simulations show that misclassifications under low illumination mainly involve difficult-to-classify images. This approach enables self-powered AI and intelligent sensors for various applications.

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