  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.

Neurons and Cognition;Neural and Evolutionary Computing;Optics