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 by 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 an efficient 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 20.4× to 45.22× 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. Real-time sensing generates large volumes of data requiring compression and reconstruction, often leading to computational bottlenecks. By leveraging algorithm–hardware co-design, Deng et al. propose an event-driven data reconstruction framework built on memristor crossbar-based analog matrix computation.