Neuromorphic Silicon Photonic Networks
This work addresses the need for ultrafast information processing in fields like radio, control, and scientific computing, representing a novel approach rather than an incremental improvement.
The authors tackled the challenge of high-performance information processing by demonstrating a recurrent silicon photonic neural network using microring weight banks, achieving a predicted 294-fold acceleration in solving a differential system emulation task compared to a conventional benchmark.
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.