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<p>AI-driven machine vision faces critical energy and latency challenges from exponential growth in visual data. This review proposes bio-inspired energy-efficient in-sensor computing utilizing emerging optoelectronic memristors, examining neural network architectures (fully connected/convolutional, recurrent, and spiking neural networks) for static, motion, and event-driven processing that directly emulates biological visual pathways. Critical integration challenges and strategic roadmaps are systematically analysed to achieve cortex-level energy efficiency in next-generation vision systems.</p>

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Bio-inspired optoelectronic devices and systems for energy-efficient in-sensor computing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月3日

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  • Xiaoting Wang,
  • Heyi Huang,
  • Jianshi Tang,
  • Ruofei Hu,
  • Yiwei Du,
  • Yuyan Wang,
  • Bin Gao,
  • He Qian,
  • Huaqiang Wu

摘要

AI-driven machine vision faces critical energy and latency challenges from exponential growth in visual data. This review proposes bio-inspired energy-efficient in-sensor computing utilizing emerging optoelectronic memristors, examining neural network architectures (fully connected/convolutional, recurrent, and spiking neural networks) for static, motion, and event-driven processing that directly emulates biological visual pathways. Critical integration challenges and strategic roadmaps are systematically analysed to achieve cortex-level energy efficiency in next-generation vision systems.