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This study introduces a novel optical convolution streaming processor (OCSP) for faster, energy-efficient AI hardware. The microcomb-enabled processor achieves 4 TOPS, addressing data center demands.

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Area of Science:

  • Photonics and Neuromorphic Computing
  • Data Center Hardware Architectures

Background:

  • Exponential growth in cloud computing and AI necessitates high-bandwidth, energy-efficient data center hardware.
  • Moore's Law limitations drive the search for alternatives like optical neuromorphic computing for ultra-high speeds and low energy consumption.

Purpose of the Study:

  • To propose and validate a microcomb-enabled parallel optical convolution streaming processor (OCSP).
  • To demonstrate a practical solution for integrating photonic computing into data center interconnects for AI workloads.

Main Methods:

  • Developed an OCSP utilizing time, space, and wavelength three-dimensional multiplexing.
  • Implemented a robust self-calibration mechanism for accurate optical phase calibration.
  • Employed time-space interleaving passive periodic interference architecture with wavelength-division multiplexing.

Main Results:

  • Achieved data rates of 50 GBaud or higher.
  • Reached a convolution computing speed of up to 4 trillion operations per second (TOPS).
  • Experimentally verified performance for parallel image feature extraction and recognition tasks.

Conclusions:

  • The OCSP provides a scalable, low-latency solution for AI workloads in data centers.
  • This technology offers a practical pathway for integrating photonic computing units into existing data center infrastructure.