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<p>Optical diffractive neural networks are emerging for improving speed and energy efficiency in machine learning. However, the challenges of nonlinear activation functions (e.g., latency issues, high power consumption, and cascading complexity) impede their performance and practical deployment. Here, we propose a programmable multilayer full-space nonlinear neural network operating in the microwave frequency band. Its nonlinear layers are constructed using programmable metasurfaces integrated with RF components, implementing a ReLU-like activation function. The nonlinear architecture achieves a nanosecond-scale delay (17.7 ns), representing orders of magnitude improvement in speed over photoelectric conversion-based nonlinearities. Moreover, the nonlinearity is characterized by exceedingly low thresholds and reconfigurable nonlinear activation functions. The system demonstrates remarkable classification capability in image classification and real-time human posture recognition tasks. Characterized by low latency, high speed, low power consumption, and flexible nonlinear activation, this architecture holds great promise for applications in security screening, medical rehabilitation, human-computer interaction, and numerous other fields.</p>

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Multilayer nonlinear diffraction neural networks with programmable and fast ReLU activation function

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月24日

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  • Yu Ming Ning,
  • Qian Ma,
  • Qiang Xiao,
  • Xin Xin Gao,
  • Qian Wen Wu,
  • Ze Gu,
  • Rui Si Li,
  • Long Chen,
  • Jian Wei You,
  • Tie Jun Cui

摘要

Optical diffractive neural networks are emerging for improving speed and energy efficiency in machine learning. However, the challenges of nonlinear activation functions (e.g., latency issues, high power consumption, and cascading complexity) impede their performance and practical deployment. Here, we propose a programmable multilayer full-space nonlinear neural network operating in the microwave frequency band. Its nonlinear layers are constructed using programmable metasurfaces integrated with RF components, implementing a ReLU-like activation function. The nonlinear architecture achieves a nanosecond-scale delay (17.7 ns), representing orders of magnitude improvement in speed over photoelectric conversion-based nonlinearities. Moreover, the nonlinearity is characterized by exceedingly low thresholds and reconfigurable nonlinear activation functions. The system demonstrates remarkable classification capability in image classification and real-time human posture recognition tasks. Characterized by low latency, high speed, low power consumption, and flexible nonlinear activation, this architecture holds great promise for applications in security screening, medical rehabilitation, human-computer interaction, and numerous other fields.