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  3. Data-in-situ Computing With One-pixel-multiple-memristor Architecture For Neuromorphic Sequential Vision.
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  3. Data-in-situ Computing With One-pixel-multiple-memristor Architecture For Neuromorphic Sequential Vision.

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Data-In-situ Computing with One-Pixel-Multiple-Memristor Architecture for Neuromorphic Sequential Vision.

Yi Sun1,2, Peiwen Tong1,2, Jiangrong Shen3

  • 1College of Electronic Science and Technology, National University of Defense Technology, Changsha, 410073, China.

Nature Communications
|March 20, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel neuromorphic vision system using a one-pixel-multiple-memristor (1PnR) architecture. This design significantly reduces latency and energy consumption for dynamic image processing, enhancing artificial vision capabilities.

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Area of Science:

  • Neuromorphic Engineering
  • Artificial Intelligence
  • Materials Science

Background:

  • Traditional neuromorphic vision systems face limitations in dynamic image processing due to inefficient pixel-to-memristor architectures and inadequate temporary storage.
  • Existing systems struggle with energy efficiency and latency, hindering real-world applications.

Purpose of the Study:

  • To propose a novel one-pixel-multiple-memristor (1PnR) architecture inspired by human visual working memory.
  • To develop a data-in-situ computing network for efficient image processing within neuromorphic vision systems.
  • To overcome the limitations of traditional architectures in terms of speed and energy consumption.

Main Methods:

  • Implemented a one-pixel-multiple-memristor (1PnR) architecture with a rolling exposure strategy for rapid sequential image acquisition.
  • Developed a data-in-situ computing network enabling direct computation on stored image data via voltage vectors.
  • Eliminated energy-intensive data transmission by performing computations directly on the memristor array.

    • Main Results:

    • Achieved 95.7% recognition accuracy on the Weizmann human action flow dataset with a hardware prototype.
    • Demonstrated an estimated 2000× reduction in latency for image sensing and storage compared to CMOS systems.
    • Showcased an estimated 160× reduction in energy consumption for image processing.

    Conclusions:

    • The proposed 1PnR architecture offers a significant advancement in energy efficiency and speed for neuromorphic vision.
    • This approach holds substantial potential for developing next-generation, low-power artificial vision systems.
    • The data-in-situ computing network effectively addresses data transmission bottlenecks in current systems.