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<p>The rapid growth of Internet of Things applications has substantially increased the number of connected sensors and data volume, yet conventional digital conversion and transmission systems impose high energy and latency costs. Here we develop a neuromorphic sensing system integrating a flexible piezoelectric haptic sensor array, event-triggered preprocessing circuitry and a memristive system on a chip. The circuitry transforms transient voltage spikes from sensor pixels into decaying voltage waveforms, generating a time surface for event-based analogue in-memory computing within the chip. Our system achieves 87%–92% recognition accuracy for patterns written on the sensor array and reduces the energy-delay product during inference compared with conventional digital platforms. These results highlight the potential of the memristive system on a chip for energy-efficient, low-latency edge processing of analogue sensor data, advancing intelligent sensing technologies.</p>

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Event-based neuromorphic sensing system with flexible haptic sensors and a memristive system on a chip

  • 期刊论文

  • 发表日期：

    2026年1月19日

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  • Wuyu Zhao,
  • Yi Huang,
  • Amit Tewari,
  • Alireza Jaberi Rad,
  • Andrew Zhang,
  • Ning Ge,
  • J. Joshua Yang,
  • Miao Hu,
  • Sayani Majumdar,
  • Qiangfei Xia

摘要

The rapid growth of Internet of Things applications has substantially increased the number of connected sensors and data volume, yet conventional digital conversion and transmission systems impose high energy and latency costs. Here we develop a neuromorphic sensing system integrating a flexible piezoelectric haptic sensor array, event-triggered preprocessing circuitry and a memristive system on a chip. The circuitry transforms transient voltage spikes from sensor pixels into decaying voltage waveforms, generating a time surface for event-based analogue in-memory computing within the chip. Our system achieves 87%–92% recognition accuracy for patterns written on the sensor array and reduces the energy-delay product during inference compared with conventional digital platforms. These results highlight the potential of the memristive system on a chip for energy-efficient, low-latency edge processing of analogue sensor data, advancing intelligent sensing technologies.