Comparison of Models for Training Optical Matrix Multipliers in Neuromorphic PICs
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 4 of its 10 references that are in the map. First posted on arXiv 23 Nov 2021.
Builds on
4 of 10 references.
References · 4
- 2020Photonics for artificial intelligence and neuromorphic computingcited by 111
- 2016Neuromorphic Silicon Photonic Networkscited by 66
- 2019Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networkscited by 19
- 2019Design of optical neural networks with component imprecisionscited by 10
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Citing papers per year: 2022: 1.
1 paper.