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<p>The exponential growth of cloud computing and artificial intelligence (AI) applications has driven an urgent need for high-bandwidth, energy-efficient hardware architectures in data centers. With Moore’s Law nearing its limits, optical neuromorphic computing hardware offers a promising alternative, providing ultra-high speeds and minimal energy consumption due to its analog architecture. Here, we propose the microcomb-enabled parallel optical convolution streaming processor (OCSP) with time, space, and wavelength three-dimensional multiplexing, operating at data rates of 50 GBaud or higher, achieving a convolution computing speed of up to 4 trillion operations per second (TOPS). Moreover, the OCSP uses a robust self-calibration mechanism to achieve accurate optical phase calibration and set-up of its convolution function. This innovative approach leverages time-space interleaving passive periodic interference architecture, incorporating wavelength-division-multiplexing technology, and is verified experimentally for parallel image feature extraction and recognition tasks. Our OCSP offers a practical pathway for seamlessly integrating photonic computing units into data center interconnects, unlocking photonic computing’s potential for scalable, low-latency AI workloads.</p>

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Microcomb-enabled parallel self- calibration optical convolution streaming processor

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月5日

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  • Jiajia Wang,
  • Xingyuan Xu,
  • Xiaotian Zhu,
  • Yifu Xu,
  • Shifan Chen,
  • Haoran Zhang,
  • Yixuan Zheng,
  • Shuying Li,
  • Yunping Bai,
  • Zhihui Liu,
  • Roberto Morandotti,
  • Brent E. Little,
  • Sai T. Chu,
  • Arthur J. Lowery,
  • David J. Moss,
  • Kun Xu

摘要

The exponential growth of cloud computing and artificial intelligence (AI) applications has driven an urgent need for high-bandwidth, energy-efficient hardware architectures in data centers. With Moore’s Law nearing its limits, optical neuromorphic computing hardware offers a promising alternative, providing ultra-high speeds and minimal energy consumption due to its analog architecture. Here, we propose the microcomb-enabled parallel optical convolution streaming processor (OCSP) with time, space, and wavelength three-dimensional multiplexing, operating at data rates of 50 GBaud or higher, achieving a convolution computing speed of up to 4 trillion operations per second (TOPS). Moreover, the OCSP uses a robust self-calibration mechanism to achieve accurate optical phase calibration and set-up of its convolution function. This innovative approach leverages time-space interleaving passive periodic interference architecture, incorporating wavelength-division-multiplexing technology, and is verified experimentally for parallel image feature extraction and recognition tasks. Our OCSP offers a practical pathway for seamlessly integrating photonic computing units into data center interconnects, unlocking photonic computing’s potential for scalable, low-latency AI workloads.