Data-efficient Modeling of Optical Matrix Multipliers Using Transfer Learning
We demonstrate transfer learning-assisted neural network models for optical matrix multipliers with scarce measurement data. Our approach uses <10\% of experimental data needed for best performance and outperforms analytical models for a Mach-Zehnder interferometer mesh.
Code (0)
등록된 구현이 없습니다.
Tasks
Transfer LearningSimilar Papers 제목 키워드 기반
Addressing Data Scarcity in Optical Matrix Multiplier Modeling Using Transfer Learning
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 app…
Transfer LearningData-driven Modeling of Mach-Zehnder Interferometer-based Optical Matrix Multipliers
Photonic integrated circuits are facilitating the development of optical neural networks, which have the potential to be both faster and more energy efficient than their electronic counterparts since optical signals are …
Comparison of Models for Training Optical Matrix Multipliers in Neuromorphic PICs
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 outperform…
Optical Transformers
The rapidly increasing size of deep-learning models has caused renewed and growing interest in alternatives to digital computers to dramatically reduce the energy cost of running state-of-the-art neural networks. Optical…
QuantizationExploiting Angular Multiplexing for Polarization-diversity in Off-axis Digital Holography
Digital holography measures the complex optical field and transfer matrix of a device, polarization-diversity is often achieved through spatial multiplexing. We introduce angular multiplexing, to increase flexibility in …
Diversity