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<p>The deployment of artificial intelligence for real-time object detection in edge applications is constrained by the power and latency limitations of conventional computing architectures. Here we show a bio-inspired neuromorphic system built around a self-selective GaO<sub>x</sub>/ZnO memristor to address this challenge. The device exhibits a selection ratio and nonlinearity (both of ~10⁷), picoampere-level leakage currents, and microsecond-scale volatile dynamics. We integrate these memristors into a 32×32 array emulating the first-spike-time-coding mechanism of the frog visual system, enabling millisecond-scale pulse responses. When applied to aerial drone object detection, our hardware system achieves reliable recognition for pedestrians and vehicles, with only a 2.5% accuracy drop compared to software simulations. Furthermore, the array demonstrates a parallel processing scale of ~8.36×10¹² computational nodes under a 10% read margin. This work provides a tangible hardware solution for constructing fast-response neuromorphic computing systems at the edge, suitable for intelligent transportation and real-time monitoring.</p>

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Volatile self-selective memristive neuron for millisecond-latency neuromorphic object detection at the edge

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月28日

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  • Zhejia Zhang,
  • Jiahua Xu,
  • Xuemeng Fan,
  • Guobin Zhang,
  • Zijian Wang,
  • Pengtao Li,
  • Qi Luo,
  • Haoxiang Yu,
  • Shuai Zhong,
  • Yunyan Zhang,
  • Wenzhang Fang,
  • Weidong You,
  • Daying Sun,
  • Kun Ren,
  • Qing Wan,
  • Yishu Zhang

摘要

The deployment of artificial intelligence for real-time object detection in edge applications is constrained by the power and latency limitations of conventional computing architectures. Here we show a bio-inspired neuromorphic system built around a self-selective GaOx/ZnO memristor to address this challenge. The device exhibits a selection ratio and nonlinearity (both of ~10⁷), picoampere-level leakage currents, and microsecond-scale volatile dynamics. We integrate these memristors into a 32×32 array emulating the first-spike-time-coding mechanism of the frog visual system, enabling millisecond-scale pulse responses. When applied to aerial drone object detection, our hardware system achieves reliable recognition for pedestrians and vehicles, with only a 2.5% accuracy drop compared to software simulations. Furthermore, the array demonstrates a parallel processing scale of ~8.36×10¹² computational nodes under a 10% read margin. This work provides a tangible hardware solution for constructing fast-response neuromorphic computing systems at the edge, suitable for intelligent transportation and real-time monitoring.