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<p>In artificial neural networks, data structures usually exist in the form of vectors, matrices, or higher-dimensional tensors. However, traditional electronic computing architectures are limited by the bottleneck of separation of storage and computing, making it difficult to efficiently handle large-scale tensor operations. The research team has developed a photonic tensor processing unit based on a single microring resonator, which performs tensor convolution operations in multiple dimensions of time, wavelength, and microwave frequency by precisely adjusting the operating state of multi-wavelength lasers. This innovative design increases the photonic computing density to 34.04 TOPS/mm², significantly surpassing the performance level of existing photonic computing chips.</p>

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Photonics and microwaves merge to improve computing flexibility

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月4日

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  • Hongwei Wang,
  • Guangwei Hu

摘要

In artificial neural networks, data structures usually exist in the form of vectors, matrices, or higher-dimensional tensors. However, traditional electronic computing architectures are limited by the bottleneck of separation of storage and computing, making it difficult to efficiently handle large-scale tensor operations. The research team has developed a photonic tensor processing unit based on a single microring resonator, which performs tensor convolution operations in multiple dimensions of time, wavelength, and microwave frequency by precisely adjusting the operating state of multi-wavelength lasers. This innovative design increases the photonic computing density to 34.04 TOPS/mm², significantly surpassing the performance level of existing photonic computing chips.