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SUANPAN: scalable photonic linear vector machine.

Ziyue Yang1, Chen Li1, Yuqia Ran2

  • 1Department of Electronic Engineering, Tsinghua University, 100084, Beijing, China.

Light, Science & Applications
|December 31, 2025
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Summary
This summary is machine-generated.

A novel photonic SUANPAN architecture enables scalable vector multiplication for artificial intelligence (AI). This photonic linear vector machine uses emitter-detector pairs for high-dimensional computations, achieving over 98% fidelity.

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Area of Science:

  • Photonics and Artificial Intelligence (AI)
  • Optical Computing
  • Linear Algebra

Background:

  • Photonics offers advantages for AI vector multiplication due to parallelism and speed.
  • Current photonic linear operations face limitations in dimensionality and complexity.
  • Digital-analog conversion is a bottleneck in existing photonic computing architectures.

Purpose of the Study:

  • To propose a programmable and reconfigurable photonic linear vector machine with extreme scalability.
  • To overcome the dimensionality limitations of current photonic linear operations.
  • To develop a photonic computing architecture inspired by the Chinese abacus (Suanpan).

Main Methods:

  • Utilizing independent emitter-detector pairs as basic computing units.
  • Employing bit encoding and analog detection for vector element preparation, avoiding large converter arrays.
  • Implementing the architecture with an 8x8 vertical cavity surface-emitting laser (VCSEL) array and an 8x8 MoTe2 photodetector array.

Main Results:

  • Achieved computing fidelities greater than 98% for vector inner products.
  • Demonstrated scalability by multiplying independent emitter-detector pairs without internal beam interaction.
  • Successfully applied the photonic SUANPAN to solve a 1024-dimensional optimization problem.
  • Attained 88% classification accuracy on a handwritten digit dataset.

Conclusions:

  • The photonic SUANPAN architecture provides extreme scalability for high-dimensional vector operations.
  • This approach bypasses the need for complex digital-analog conversion arrays.
  • The photonic SUANPAN shows potential as a fundamental linear vector machine for diverse AI applications.