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Coherent optical neural network chip with novel computing model for large-scale matrix-vector multiplication

Ye Zhang1, Lei Yu2, Meng Guo3

  • 1Beijing Information Science and Technology University, Beijing, 100192, China.

Neural networks : the official journal of the International Neural Network Society
|December 19, 2025
PubMed
まとめ

This study introduces an innovative optical neural network (ONN) chip that enhances computing efficiency and accuracy for image classification tasks. The novel design achieves high performance on datasets like MNIST, demonstrating robustness and generalizability.

キーワード:
Coherent detectionImage classificationMatrix-vector multiplicationOptical neural network

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科学分野:

  • Optoelectronics
  • Artificial Intelligence
  • Computer Engineering

背景:

  • Optical neural networks (ONNs) offer potential for high-speed computation but face challenges in complexity and efficiency.
  • Existing ONN designs often require complex phase compensation and encoding/decoding steps.

研究 の 目的:

  • To propose an innovative optical neural network (ONN) chip utilizing a coherent detection structure.
  • To enhance computational efficiency and accuracy in ONNs by simplifying the operational process.

主な方法:

  • Developed a novel ONN chip architecture based on coherent detection.
  • Established a mathematical model to integrate transformation functions into activation functions, eliminating phase compensation.
  • Reduced encoding and decoding complexity.

主要な成果:

  • Achieved 97.28% accuracy on the MNIST dataset, comparable to conventional computer-based results.
  • Demonstrated high computing efficiency and maintained accuracy.
  • Showcased strong robustness and generalizability across various network architectures.

結論:

  • The proposed ONN chip design offers a significant advancement in optical computing.
  • The integrated approach simplifies ONN operation while preserving high performance.
  • This technology holds promise for efficient and accurate image classification and broader AI applications.