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An optical tensor core architecture for neural network training based on dual-layer waveguide topology and homodyne detection [Early Posting]

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摘要

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. The dual-layer waveguide topology is adopted to feed data into these dot-product units 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 is verified with numerical simulations.

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作者单位:

    State Key Laboratory of Advanced Optical Communication Systems and Networks
    Shanghai Jiao Tong University

引用该论文

Xu Shaofu,Zou Weiwen. An optical tensor core architecture for neural network training based on dual-layer waveguide topology and homodyne detection[J].Chinese Optics Letters,2021,19(8):08.