Meta-Learning for On-chip Photonic Neural Network Training

We introduce photonics neural-network (PNN) meta-learning to reduce on-chip optimization steps for customized tasks. We demonstrate > 4x acceleration and higher accuracy. We further propose sparsified PNN meta-learning that only optimize < 10% of hardware elements.

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.1364/cleo_at.2025.aa128_5
Primary Topic
Neural Networks and Reservoir Computing
Type
article
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article

Meta-Learning for On-chip Photonic Neural Network Training

Carson Valdez, Matthew Ho, Olav Solgaard, Zhanghao Sun
Neural Networks and Reservoir Computing
article

Meta-Learning for On-chip Photonic Neural Network Training

Carson Valdez, Matthew Ho, Olav Solgaard, Zhanghao Sun
article en

Abstract

We introduce photonics neural-network (PNN) meta-learning to reduce on-chip optimization steps for customized tasks. We demonstrate > 4x acceleration and higher accuracy. We further propose sparsified PNN meta-learning that only optimize < 10% of hardware elements.

Openalex Percentile: Top 87%
Neural Networks and Reservoir Computing
DOISourceOpenAlex
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Meta-Learning for On-chip Photonic Neural Network Training — Carson Valdez, Matthew Ho, et al. · (2025) | TGRS Research Map | TGRS