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Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification
Open accessAbstract
Convolutional neural networks (CNNs) excel in a wide variety of computer vision applications, but their high performance also comes at a high computational cost. Despite efforts to increase efficiency both algorithmically and with specialized hardware, it remains difficult to deploy CNNs in embedded systems due to tight power budgets. Here we explore a complementary strategy that incorporates a layer of optical computing prior to electronic computing, improving performance on image classification tasks while adding minimal electronic computational cost or processing time. We propose a design for an optical convolutional layer based on an optimized diffractive optical element and test our design in two simulations: a learned optical correlator and an optoelectronic two-layer CNN. We demonstrate in simulation and with an optical prototype that the classification accuracies of our optical systems rival those of the analogous electronic implementations, while providing substantial savings on computational cost.
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Cites
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Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
In-Datacenter Performance Analysis of a Tensor Processing Unit
Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., Borchers, A., et al. (2017) · Proceedings of the 44th Annual International Symposium on Computer Architecture · doi:10.1145/3079856.3080246 · Cited by 4,537
Photonic Floquet topological insulators
Rechtsman, M. C., Zeuner, J. M., Plotnik, Y., Lumer, Y., Podolsky, D., Dreisow, F., Nolte, S., Segev, M., & Szameit, A. (2013) · Nature · doi:10.1038/nature12066 · Cited by 3,332
All-optical machine learning using diffractive deep neural networks
Lin, X., Rivenson, Y., Yardimci, N. T., Veli, M., Luo, Y., Jarrahi, M., & Ozcan, A. (2018) · Science · doi:10.1126/science.aat8084 · Cited by 2,568
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Ashtiani, F., Geers, A. J., & Aflatouni, F. (2022) · Nature · doi:10.1038/s41586-022-04714-0 · Cited by 699
Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks
Williamson, I. A. D., Hughes, T. W., Minkov, M., Bartlett, B., Pai, S., & Fan, S. (2020) · IEEE Journal of Selected Topics in Quantum Electronics · doi:10.1109/jstqe.2019.2930455 · Cited by 346
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Chen, Y., Nazhamaiti, M., Xu, H., Meng, Y., Zhou, T., Li, G., Fan, J., Wei, Q., Wu, J., Qiao, F., et al. (2023) · Nature · doi:10.1038/s41586-023-06558-8 · Cited by 298
An optical neural network using less than 1 photon per multiplication
Wang, T., Ma, S.-Y., Wright, L. G., Onodera, T., Richard, B. C., & McMahon, P. L. (2022) · Nature Communications · doi:10.1038/s41467-021-27774-8 · Cited by 268
Metadata
- Authors
- Chang, J., Sitzmann, V., Dun, X., Heidrich, W., & Wetzstein, G.
- Venue
- Scientific Reports
- Published
- 2018-08-17
- Volume
- 8
- Issue
- 1
- Pages
- 12324-12324
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2887566712
- Cited by
- 564
- References
- 6
- FWCI
- 20.09
- Type
- journal article
- Language
- en
- Open access
- gold
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