Memristive crossbar array-based hardware framework for compressed sensing and event-driven neuromorphic processing

Compressed sensing (CS) enables efficient data acquisition and implicit encryption; however, its recovery stage remains a significant computational bottleneck, as it requires solving large-scale optimization problems or running iterative reconstruction algorithms. Here, we propose an event-driven CS recovery framework developed through an algorithm–hardware co-design approach. This framework employs memristor crossbar array (MCA)-based analog matrix computing (AMC) circuits as the hardware platform and incorporates a novel CS recovery algorithm (named constrained gradient descent (CGD) algorithm) designed to leverage them. Furthermore, the framework supports event-driven selective recovery via MCA-based feature detection. We fabricated the hardware to validate the proposed framework, and the experimental results demonstrate 15.49–34.21× improvements in energy efficiency over state-of-the-art methods for image and ECG signal processing. These results underscore the potential of the proposed framework as a competitive hardware solution for real-time sensing signal processing in edge devices.