The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Digital Electronics and Analog Photonics for Convolutional Neural Networks (DEAP-CNNs)
Open accessAbstract
Convolutional Neural Networks (CNNs) are powerful and highly ubiquitous tools for extracting features from large datasets for applications such as computer vision and natural language processing. However, a convolution is a computationally expensive operation in digital electronics. In contrast, neuromorphic photonic systems, which have experienced a recent surge of interest over the last few years, propose higher bandwidth and energy efficiencies for neural network training and inference. Neuromorphic photonics exploits the advantages of optical electronics, including the ease of analog processing, and busing multiple signals on a single waveguide at the speed of light. Here, we propose a Digital Electronic and Analog Photonic (DEAP) CNN hardware architecture that has potential to be 2.8 to 14 times faster while using almost 25% less energy than current state-of-the-art graphical processing units (GPUs).
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
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A 128 Gb/s PAM4 Silicon Microring Modulator With Integrated Thermo-Optic Resonance Tuning
Sun, J., Kumar, R., Sakib, M., Driscoll, J. B., Jayatilleka, H., & Rong, H. (2019) · Journal of Lightwave Technology · doi:10.1109/jlt.2018.2878327 · Cited by 292
Feedback control for microring weight banks
Tait, A. N., Jayatilleka, H., De Lima, T. F., Ma, P. Y., Nahmias, M. A., Shastri, B. J., Shekhar, S., Chrostowski, L., & Prucnal, P. R. (2018) · Optics Express · doi:10.1364/oe.26.026422 · Cited by 138
Self-citationAdversarial Noise Layer: Regularize Neural Network by Adding Noise
You, Z., Ye, J., Li, K., Xu, Z., & Wang, P. (2019) · 2019 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2019.8803055 · Cited by 80
Cited by
Parallel convolutional processing using an integrated photonic tensor core
Feldmann, J., Youngblood, N., Karpov, M., Gehring, H., Li, X., Stappers, M., Le Gallo, M., Fu, X., Lukashchuk, A., Raja, A. S., et al. (2021) · Nature · doi:10.1038/s41586-020-03070-1 · Cited by 1,900
Photonics for artificial intelligence and neuromorphic computing
Shastri, B. J., Tait, A. N., Ferreira de Lima, T., Pernice, W. H. P., Bhaskaran, H., Wright, C. D., & Prucnal, P. R. (2021) · Nature Photonics · doi:10.1038/s41566-020-00754-y · Cited by 1,756
Self-citationWu, C., Yu, H., Lee, S., Peng, R., Takeuchi, I., & Li, M. (2021) · Nature Communications · doi:10.1038/s41467-020-20365-z · Cited by 373
Prospects and applications of on-chip lasers
Zhou, Z., Ou, X., Fang, Y., Alkhazraji, E., Xu, R., Wan, Y., & Bowers, J. E. (2023) · eLight · doi:10.1186/s43593-022-00027-x · Cited by 319
A silicon photonic–electronic neural network for fibre nonlinearity compensation
Huang, C., Fujisawa, S., de Lima, T. F., Tait, A. N., Blow, E. C., Tian, Y., Bilodeau, S., Jha, A., Yaman, F., Peng, H.-T., et al. (2021) · Nature Electronics · doi:10.1038/s41928-021-00661-2 · Cited by 303
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Metadata
- Authors
- Bangari, V., Marquez, B. A., Miller, H., Tait, A. N., Nahmias, M. A., de Lima, T. F., Peng, H.-T., Prucnal, P. R., & Shastri, B. J.
- Venue
- IEEE Journal of Selected Topics in Quantum Electronics
- Published
- 2020-01
- Volume
- 26
- Issue
- 1
- Pages
- 1-13
- ISSN
- 1077-260X (ISSN-L)
- 1558-4542
- OpenAlex
- W2954910491
- Cited by
- 241
- References
- 6
- FWCI
- 11.86
- Type
- journal article
- Language
- en
- Open access
- green
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- Bangari, V., Marquez, B. A., Miller, H., Tait, A. N., Nahmias, M. A., de Lima, T. F., Peng, H.-T., Prucnal, P. R., & Shastri, B. J. (2020). Digital Electronics and Analog Photonics for Convolutional Neural Networks (DEAP-CNNs). IEEE Journal of Selected Topics in Quantum Electronics. https://doi.org/10.1109/jstqe.2019.2945540
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