Neuromorphic Silicon Photonic Networks

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 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.