Comparison of Models for Training Optical Matrix Multipliers in Neuromorphic PICs
arXiv:2111.14787v17 citations
Originality Synthesis-oriented
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This work addresses the challenge of optimizing programmable photonic chips for neuromorphic computing, though it is incremental as it focuses on model comparison.
The authors compared physics-based and neural-network models for training optical matrix multipliers in neuromorphic photonic integrated circuits, finding that the neural-network model achieved higher testing accuracy on a chip with thermal crosstalk.
We experimentally compare simple physics-based vs. data-driven neural-network-based models for offline training of programmable photonic chips using Mach-Zehnder interferometer meshes. The neural-network model outperforms physics-based models for a chip with thermal crosstalk, yielding increased testing accuracy.