  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.
