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.
Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, particularly related to processor latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges.
Due to challenging efficiency limits facing conventional and unconventional electronic architectures, information processors based on photonics have attracted renewed interest. Research communities have yet to settle on definitive techniques to describe the performance of this class of information processors. Photonic systems are different from electronic ones, and the existing concepts of computer performance measurement cannot necessarily apply. In this paper, we quantify the power use of photonic neural networks with state-of-the-art and future hardware. We derive scaling laws, physical limits, and platform platform performance metrics. We find that overall performance takes on different dominant scaling laws depending on scale, bandwidth, and resolution, which means that energy efficiency characteristics of a photonic processor can be completely described by no less than seven performance metrics over the range of relevant operating domains. The introduction of these analytical strategies provides a much needed foundation and reference for quantitative roadmapping and commercial value assignment for silicon photonic neural networks.
Silicon defect centers are promising candidates for waveguide-integrated silicon light sources. We demonstrate microresonator- and waveguide-coupled photoluminescence from silicon W centers. Microphotoluminescence measurements indicate wavelengths on-resonance with resonator modes are preferentially coupled to an adjacent waveguide. Quality factors of at least 5,300 are measured, and free spectral ranges closely match expectation. The W center phonon sideband can be used as a spectral diagnostic for a broader range of waveguide-based devices on cryogenic silicon photonic platforms.