%0 Generic %1 tait2016neuromorphic %A Tait, Alexander N. %A de Lima, Thomas Ferreira %A Zhou, Ellen %A Wu, Allie X. %A Nahmias, Mitchell A. %A Shastri, Bhavin J. %A Prucnal, Paul R. %D 2016 %K quantumnuralnet %R 10.1038/s41598-017-07754-z %T Neuromorphic Silicon Photonic Networks %U http://arxiv.org/abs/1611.02272 %X 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.

@misc{tait2016neuromorphic, abstract = {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.}, added-at = {2018-01-15T21:27:55.000+0100}, author = {Tait, Alexander N. and de Lima, Thomas Ferreira and Zhou, Ellen and Wu, Allie X. and Nahmias, Mitchell A. and Shastri, Bhavin J. and Prucnal, Paul R.}, biburl = {https://www.bibsonomy.org/bibtex/2a38e1a5e7d8bee998564bcd7696fe15e/mardukasoka}, description = {Neuromorphic Silicon Photonic Networks}, doi = {10.1038/s41598-017-07754-z}, interhash = {8455e170c7319000a949717dfd54ed14}, intrahash = {a38e1a5e7d8bee998564bcd7696fe15e}, keywords = {quantumnuralnet}, note = {cite arxiv:1611.02272Comment: 12 pages, 4 figures, accepted in Scientific Reports}, timestamp = {2018-01-15T21:27:55.000+0100}, title = {Neuromorphic Silicon Photonic Networks}, url = {http://arxiv.org/abs/1611.02272}, year = 2016 }