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Optica Publishing Group
  • Chinese Optics Letters
  • Vol. 19,
  • Issue 8,
  • pp. 082501-
  • (2021)

Optical tensor core architecture for neural network training based on dual-layer waveguide topology and homodyne detection

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Abstract

We propose an optical tensor core (OTC) architecture for neural network training. The key computational components of the OTC are the arrayed optical dot-product units (DPUs). The homodyne-detection-based DPUs can conduct the essential computational work of neural network training, i.e., matrix-matrix multiplication. Dual-layer waveguide topology is adopted to feed data into these DPUs with ultra-low insertion loss and cross talk. Therefore, the OTC architecture allows a large-scale dot-product array and can be integrated into a photonic chip. The feasibility of the OTC and its effectiveness on neural network training are verified with numerical simulations.

© 2021 Chinese Laser Press

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