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On-Chip Optical Convolutional Neural Networks

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arxiv 1808.03303 v2 pith:5FBMOCRV submitted 2018-08-09 cs.ET

Hengameh Bagherian , Scott Skirlo , Yichen Shen , Huaiyu Meng , Vladimir Ceperic , Marin Soljacic This is my paper · ORCID
classification cs.ET
keywords networksneuralconvolutionalannsarchitectureartificialcircuitclass
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Convolutional Neural Networks (CNNs) are a class of Artificial Neural Networks(ANNs) that employ the method of convolving input images with filter-kernels for object recognition and classification purposes. In this paper, we propose a photonics circuit architecture which could consume a fraction of energy per inference compared with state of the art electronics.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Efficient training for large-scale optical neural network using an evolutionary strategy and attention pruning

    cs.LG 2025-05 conditional novelty 6.0 of 10

    A CMA-ES plus attention-pruning training algorithm for block optical neural networks prunes 60-80% of parameters with under 5% accuracy loss and shows improved noise robustness.

  2. Efficient training and design of photonic neural network through neuroevolution

    cs.NE 2019-08 conditional novelty 4.0 of 10

    Neuroevolution with genetic algorithms and particle swarm optimization can train simulated optical neural networks to accuracies comparable to gradient-based methods on small classification tasks.

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