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.
Highlights
Light forms the global backbone of information transmission yet is rarely used for information transformation
Photonic devices have found analog signal processing niches where electronics can no longer satisfy demands for bandwidth and reconfigurability. This situation is exemplified by radio frequency (RF) processing, in which front-ends have come to be limited by RF electronics, analog-to-digital converters (ADCs), and digital signal processors (DSP)[15, 16]
We demonstrate a broadcast-and-weight system configured by microring weight banks that is isomorphic to a continuous-time recurrent neural network (CTRNN) model
Summary
Light forms the global backbone of information transmission yet is rarely used for information transformation. Photonic devices have found analog signal processing niches where electronics can no longer satisfy demands for bandwidth and reconfigurability. This situation is exemplified by radio frequency (RF) processing, in which front-ends have come to be limited by RF electronics, analog-to-digital converters (ADCs), and digital signal processors (DSP)[15, 16]. A conventional gate has simultaneous meaning as an abstract logical operation and as an arrangement of electronic semiconductors and thereby acts as a conduit between device engineering and computational performance In another case, neuromorphic electronics adopt unconventional standards defining spiking neural networks as event-based packet networks[23,24,25]. The fact that distributed interconnection and analog dynamics are performance strongsuits of photonic physics motivates the study of neuromorphic photonics
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