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Analysis of Optical Loss and Crosstalk Noise in MZI-based Coherent Photonic Neural Networks
The proposed models can be applied to any SP-NN architecture with different configurations to analyze the effect of loss and crosstalk.
GHOST: A Graph Neural Network Accelerator using Silicon Photonics
Graph neural networks (GNNs) have emerged as a powerful approach for modelling and learning from graph-structured data.
Characterization and Optimization of Integrated Silicon-Photonic Neural Networks under Fabrication-Process Variations
Simulation results for two example SPNNs of different scales under realistic and correlated FPVs indicate that the optimized MZIs can improve the inferencing accuracy by up to 93. 95% for the MNIST handwritten digit dataset -- considered as an example in this paper -- which corresponds to a <0. 5% accuracy loss compared to the variation-free case.
LoCI: An Analysis of the Impact of Optical Loss and Crosstalk Noise in Integrated Silicon-Photonic Neural Networks
Compared to electronic accelerators, integrated silicon-photonic neural networks (SP-NNs) promise higher speed and energy efficiency for emerging artificial-intelligence applications.
