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<p>Optical neural networks are considered next-generation physical implementations of artificial neural networks, but their capabilities are limited by on-chip integration scale and requirement for coherent light sources. This study proposes a spectral convolutional neural network (SCNN) with matter meta-imaging. The optical convolutional layer is implemented by integrating very large-scale and pixel-aligned spectral filters on CMOS image sensor. It facilitates highly parallel spectral vector-inner products of incident incoherent natural light i.e., the direct information carrier, which empowers in-sensor optical analog computing at extremely high energy efficiency. To the best of our knowledge, this is the first integrated optical computing utilizing natural light. We employ the same SCNN chip for completely different real-world complex tasks and achieve accuracies of over 96% for pathological diagnosis and almost 100% for face anti-spoofing at video rates. These results indicate a feasible and scalable in-sensor edge computing chip of natural light for various portable terminals.</p>

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Spectral convolutional neural network chip for in-sensor edge computing of incoherent natural light

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月2日

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  • Kaiyu Cui,
  • Shijie Rao,
  • Sheng Xu,
  • Yidong Huang,
  • Xusheng Cai,
  • Zhilei Huang,
  • Yu Wang,
  • Xue Feng,
  • Fang Liu,
  • Wei Zhang,
  • Yali Li,
  • Shengjin Wang

摘要

Optical neural networks are considered next-generation physical implementations of artificial neural networks, but their capabilities are limited by on-chip integration scale and requirement for coherent light sources. This study proposes a spectral convolutional neural network (SCNN) with matter meta-imaging. The optical convolutional layer is implemented by integrating very large-scale and pixel-aligned spectral filters on CMOS image sensor. It facilitates highly parallel spectral vector-inner products of incident incoherent natural light i.e., the direct information carrier, which empowers in-sensor optical analog computing at extremely high energy efficiency. To the best of our knowledge, this is the first integrated optical computing utilizing natural light. We employ the same SCNN chip for completely different real-world complex tasks and achieve accuracies of over 96% for pathological diagnosis and almost 100% for face anti-spoofing at video rates. These results indicate a feasible and scalable in-sensor edge computing chip of natural light for various portable terminals.