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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.

Abstract Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive uncertainty assessment. However, because of their probabilistic nature, they are computationally intensive. An innovative solution utilizes memristors’ inherent probabilistic nature to implement Bayesian neural networks. However, when using memristors, statistical effects follow the laws of device physics, whereas in Bayesian neural networks, those effects can take arbitrary shapes. This work overcome this difficulty by adopting a variational inference training augmented by a “technological loss”, incorporating memristor physics. This technique enabled programming a Bayesian neural network on 75 crossbar arrays of 1,024 memristors, incorporating CMOS periphery for in-memory computing. The experimental neural network classified heartbeats with high accuracy, and estimated the certainty of its predictions. The results reveal orders-of-magnitude improvement in inference energy efficiency compared to a microcontroller or an embedded graphics processing unit performing the same task.

Crossbars of resistive memories, or memristors, provide a road to reduce the energy consumption of artificial neural networks, by naturally implementing multiply accumulate operations, their most basic calculations. However, a major challenge of implementing robust hardware neural networks is the conductance instability over time of resistive memories, due to the local recombination of oxygen vacancies. This effect causes resistive memory‐based neural networks to rapidly lose accuracy, an issue that is sometimes overlooked. Herein, this conductance instability issue is shown, which can be avoided without changing the material stack of the resistive memory by exploiting an original programming strategy. This technique relies on program‐and‐verify loops with appropriately chosen wait times and ensures that the resistive memories are programmed into states with stable filaments. To test the strategy, a 32 × 32 in‐memory computing system, fabricated in a hybrid complementary metal‐oxide‐semiconductor (CMOS)/hafnium oxide technology, is programmed to classify heart arrhythmia from electrocardiogram. When the resistive memories are programmed conventionally, the system loses accuracy within hours. In contrast, when using this technique, the system maintains an accuracy of 95% over more than 2 months. These results highlight the potential of resistive memory for the implementation of low‐power neural networks with long‐term stability.