The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
Neuromorphic photonic networks using silicon photonic weight banks
Open accessAbstract
Photonic systems for high-performance information processing have attracted renewed interest. Neuromorphic silicon photonics has the potential to integrate processing functions that vastly exceed the capabilities of electronics. We report first observations of a recurrent silicon photonic neural network, in which connections are configured by microring weight banks. A mathematical isomorphism between the silicon photonic circuit and a continuous neural network model is demonstrated through dynamical bifurcation analysis. Exploiting this isomorphism, a simulated 24-node silicon photonic neural network is programmed using “neural compiler” to solve a differential system emulation task. A 294-fold acceleration against a conventional benchmark is predicted. We also propose and derive power consumption analysis for modulator-class neurons that, as opposed to laser-class neurons, are compatible with silicon photonic platforms. At increased scale, Neuromorphic silicon photonics could access new regimes of ultrafast information processing for radio, control, and scientific computing.
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“Neural” computation of decisions in optimization problems
Hopfield, J. J., & Tank, D. W. (1985) · Biological Cybernetics · doi:10.1007/bf00339943 · Cited by 6,031
A million spiking-neuron integrated circuit with a scalable communication network and interface
Merolla, P. A., Arthur, J. V., Alvarez-Icaza, R., Cassidy, A. S., Sawada, J., Akopyan, F., Jackson, B. L., Imam, N., Guo, C., Nakamura, Y., et al. (2014) · Science · doi:10.1126/science.1254642 · Cited by 4,147
Deep learning with coherent nanophotonic circuits
Shen, Y., Harris, N. C., Skirlo, S., Prabhu, M., Baehr-Jones, T., Hochberg, M., Sun, X., Zhao, S., Larochelle, H., Englund, D., et al. (2017) · Nature Photonics · doi:10.1038/nphoton.2017.93 · Cited by 3,340
TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip
Akopyan, F., Sawada, J., Cassidy, A., Alvarez-Icaza, R., Arthur, J., Merolla, P., Imam, N., Nakamura, Y., Datta, P., Nam, G.-J., et al. (2015) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2015.2474396 · Cited by 1,687
Thomson, D., Zilkie, A., Bowers, J. E., Komljenovic, T., Reed, G. T., Vivien, L., Marris-Morini, D., Cassan, E., Virot, L., Fédéli, J.-M., et al. (2016) · Journal of Optics · doi:10.1088/2040-8978/18/7/073003 · Cited by 1,363
Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations
Benjamin, B. V., Gao, P., McQuinn, E., Choudhary, S., Chandrasekaran, A. R., Bussat, J.-M., Alvarez-Icaza, R., Arthur, J. V., Merolla, P. A., & Boahen, K. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2313565 · Cited by 1,266
Memory and Information Processing in Neuromorphic Systems
Indiveri, G., & Liu, S.-C. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2444094 · Cited by 1,037
Parallel photonic information processing at gigabyte per second data rates using transient states
Brunner, D., Soriano, M. C., Mirasso, C. R., & Fischer, I. (2013) · Nature Communications · doi:10.1038/ncomms2368 · Cited by 938
Experimental demonstration of reservoir computing on a silicon photonics chip
Vandoorne, K., Mechet, P., Van Vaerenbergh, T., Fiers, M., Morthier, G., Verstraeten, D., Schrauwen, B., Dambre, J., & Bienstman, P. (2014) · Nature Communications · doi:10.1038/ncomms4541 · Cited by 892
Photonic analog-to-digital converters
Valley, G. C. (2007) · Optics Express · doi:10.1364/oe.15.001955 · Cited by 784
Cited by
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-citationAll-optical spiking neurosynaptic networks with self-learning capabilities
Feldmann, J., Youngblood, N., Wright, C. D., Bhaskaran, H., & Pernice, W. H. P. (2019) · Nature · doi:10.1038/s41586-019-1157-8 · Cited by 1,529
Review of Silicon Photonics Technology and Platform Development
Siew, S. Y., Li, B., Gao, F., Zheng, H. Y., Zhang, W., Guo, P., Xie, S. W., Song, A., Dong, B., Luo, L. W., et al. (2021) · Journal of Lightwave Technology · doi:10.1109/jlt.2021.3066203 · Cited by 988
An on-chip photonic deep neural network for image classification
Ashtiani, F., Geers, A. J., & Aflatouni, F. (2022) · Nature · doi:10.1038/s41586-022-04714-0 · Cited by 699
An optical neural chip for implementing complex-valued neural network
Zhang, H., Gu, M., Jiang, X. D., Thompson, J., Cai, H., Paesani, S., Santagati, R., Laing, A., Zhang, Y., Yung, M. H., et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20719-7 · Cited by 666
Metadata
- Authors
- Tait, A. N., de Lima, T. F., Zhou, E., Wu, A. X., Nahmias, M. A., Shastri, B. J., & Prucnal, P. R.
- Venue
- Scientific Reports
- Published
- 2017-08-07
- Volume
- 7
- Issue
- 1
- Pages
- 7430-7430
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2739588406
- Cited by
- 849
- References
- 76
- FWCI
- 31.49
- Type
- journal article
- Language
- en
- Open access
- gold
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