Neuromorphic Silicon Photonic Networks
Alexander N. Tait, Thomas Ferreira de Lima, Ellen Zhou, Allie X. Wu,, Mitchell A. Nahmias, Bhavin J. Shastri, and Paul R. Prucnal

TL;DR
This paper demonstrates the first silicon photonic neural network with recurrent connections, showing potential for ultrafast information processing and significant acceleration over traditional systems.
Contribution
It introduces a silicon photonic neural network with microring weight banks and demonstrates its mathematical isomorphism to neural models, including simulation results and power analysis.
Findings
First observation of a recurrent silicon photonic neural network
Simulated 24-node network predicts 294-fold acceleration
Power consumption analysis for modulator-class neurons
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…
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