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We developed a photonic tensor processor for faster artificial intelligence (AI) computations. This optical system achieves high accuracy in deep neural network tasks, offering a scalable solution for AI acceleration.

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Area of Science:

  • Photonics
  • Artificial Intelligence
  • Computer Engineering

Background:

  • Artificial neural networks (ANNs) drive advancements in AI but require efficient tensor computations.
  • Analog photonic systems offer ultra-fast, low-latency computing, overcoming limitations of digital electronics.

Purpose of the Study:

  • To present a photonic tensor processor for deep neural network inference.
  • To demonstrate a compact, integrated, and reprogrammable platform for high-speed optical AI accelerators.

Main Methods:

  • Developed an all-optical crossbar with nine inputs and three outputs for parallel signal accumulation.
  • Integrated the processor into a standard 19-inch rack unit with a PyTorch interface.
  • Fabricated the chip using silicon photonics, incorporating electro-absorption modulators and photodiodes.

Main Results:

  • Achieved 98.1% accuracy on MNIST and 72.0% accuracy on CIFAR-10 datasets.
  • Demonstrated the processor's capability for deep neural network inference.
  • Highlighted the system's scalability and compatibility with high-volume manufacturing.

Conclusions:

  • The photonic tensor processor represents a significant step toward scalable, high-speed optical AI acceleration.
  • The compact and reprogrammable platform enables seamless hardware deployment for AI applications.
  • Analog photonic computing offers a promising path for future AI hardware.