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Neuromorphic photonic networks using silicon photonic weight banks

Author(s): Tait, AN; De Lima, TF; Zhou, E; Wu, AX; Nahmias, MA; et al

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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.
Publication Date: 2017
Citation: Tait, AN, De Lima, TF, Zhou, E, Wu, AX, Nahmias, MA, Shastri, BJ, Prucnal, PR. (2017). Neuromorphic photonic networks using silicon photonic weight banks. Scientific Reports, 7 (10.1038/s41598-017-07754-z
DOI: doi:10.1038/s41598-017-07754-z
Type of Material: Journal Article
Journal/Proceeding Title: Scientific Reports
Version: Author's manuscript

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