Neuromorphic photonic networks using silicon photonic weight banks
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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.
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The record
- Venue
- Scientific Reports
- Topic
- Neural Networks and Reservoir Computing
- Field
- Computer Science
- Canadian institutions
- —
- Funders
- Natural Sciences and Engineering Research Council of CanadaUniversity of WashingtonNational Science Foundation
- Keywords
- Neuromorphic engineeringPhotonicsComputer scienceSilicon photonicsEmulationArtificial neural networkSiliconBenchmark (surveying)Electronic engineeringMaterials scienceArtificial intelligenceOptoelectronicsEngineering
- Has abstract in OpenAlex
- yes