Addressing Data Scarcity in Optical Matrix Multiplier Modeling Using Transfer Learning

• URL: http://arxiv.org/abs/2308.11630v2
• Date: Mon, 13 Nov 2023 15:58:10 GMT
• Title: Addressing Data Scarcity in Optical Matrix Multiplier Modeling Using Transfer Learning
• Authors: Ali Cem, Ognjen Jovanovic, Siqi Yan, Yunhong Ding, Darko Zibar, and Francesco Da Ros
• Abstract summary: We present and experimentally evaluate using transfer learning to address experimental data scarcity. Our approach involves pre-training the model using synthetic data generated from a less accurate analytical model. We achieve 1 dB root-mean-square error on the matrix weights implemented by a 3x3 photonic chip while using only 25% of the available data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present and experimentally evaluate using transfer learning to address experimental data scarcity when training neural network (NN) models for Mach-Zehnder interferometer mesh-based optical matrix multipliers. Our approach involves pre-training the model using synthetic data generated from a less accurate analytical model and fine-tuning with experimental data. Our investigation demonstrates that this method yields significant reductions in modeling errors compared to using an analytical model, or a standalone NN model when training data is limited. Utilizing regularization techniques and ensemble averaging, we achieve < 1 dB root-mean-square error on the matrix weights implemented by a 3x3 photonic chip while using only 25% of the available data.

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