Neuromorphic photonic networks using silicon photonic weight banks

Alexander N. Tait, Thomas Ferreira De Lima, Ellen Zhou, Allie X. Wu, Mitchell A. Nahmias, Bhavin J. Shastri, Paul R. Prucnal

Research output: Contribution to journalArticle

137 Scopus citations

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.

Original languageEnglish (US)
Article number7430
JournalScientific reports
Volume7
Issue number1
DOIs
StatePublished - Dec 1 2017

All Science Journal Classification (ASJC) codes

  • General

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