Chinese Optics Letters, 2021, 19 (8): 082501, Published Online: Apr. 20, 2021   

Optical tensor core architecture for neural network training based on dual-layer waveguide topology and homodyne detection Download: 782次

Author Affiliations
State Key Laboratory of Advanced Optical Communication Systems and Networks, Intelligent Microwave Lightwave Integration Innovation Center (iMLic), Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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.

Shaofu Xu, Weiwen Zou. Optical tensor core architecture for neural network training based on dual-layer waveguide topology and homodyne detection[J]. Chinese Optics Letters, 2021, 19(8): 082501.

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