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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月25日

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  • Mingxin Deng,
  • Yinan Wang,
  • Hui Xu,
  • Likang Sun,
  • Lun Lu,
  • Ao Xu,
  • Nuo Xu,
  • Zhiwei Li,
  • Qingjiang Li,
  • Kyung Min Kim

摘要

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.