Deep photonic reservoir computing based on a distributed feedback laser array

Abstract

Photonic reservoir computing (RC) is emerging as a competitive candidate for ultra-fast and energy-efficient neuromorphic computing, noted for its physical compatibility and straightforward training process. However, this widely appreciated form of machine learning typically employs only a single hidden layer with a feedback loop (FL), which essentially constrains the capability for complex task processing and poses challenges to integration. Here, we present an integrated deep photonic RC setup based on a distributed feedback laser array. This setup eliminates the dependence of the FL through the pre-processing termed quasi-convolution coding (QC), where the QC-based RC (QRC) significantly reduces the complexity of the network under the ensured performance. In particular, we extend this QRC into an on-chip deep structure, where this deep QRC (DQRC) exhibits remarkable superiority to its shallow counterparts. We demonstrate the augmented capabilities of DQRC through both simulations and experimental comparisons with QRC, extreme learning machine, and time-delay RC in time-dependent tasks. Moreover, the proposed deep configuration also excels in static image processing. We confirm the potential for simplifying the hardware implementations of deep neural networks, revealing a promising solution to satisfy the urgent demand for high-integration brain-inspired systems.