On-chip wave chaos for photonic extreme learning
This work addresses the problem of hardware scalability and energy efficiency for neural networks, offering a domain-specific photonic solution that is incremental in nature.
The authors tackled the need for scalable and energy-efficient neural networks by experimentally demonstrating a chip-scale photonic extreme learning machine using wave chaos interference in a stadium microcavity, achieving classification performance optimized for four benchmark tasks by controlling the readout size.
The increase in demand for scalable and energy efficient artificial neural networks has put the focus on novel hardware solutions. Integrated photonics offers a compact, parallel and ultra-fast information processing platform, specially suited for extreme learning machine (ELM) architectures. Here we experimentally demonstrate a chip-scale photonic ELM based on wave chaos interference in a stadium microcavity. By encoding the input information in the wavelength of an external single-frequency tunable laser source, we leverage the high sensitivity to wavelength of injection in such photonic resonators. We fabricate the microcavity with direct laser writing of SU-8 polymer on glass. A scattering wall surrounding the stadium operates as readout layer, collecting the light associated with the cavity's leaky modes. We report uncorrelated and aperiodic behavior in the speckles of the scattering barrier from a high resolution scan of the input wavelength. Finally, we characterize the system's performance at classification in four qualitatively different benchmark tasks. As we can control the number of output nodes of our ELM by measuring different parts of the scattering barrier, we demonstrate the capability to optimize our photonic ELM's readout size to the performance required for each task.