Parallel nonlinear neuromorphic computing with temporal encoding

The proliferation of deep learning applications has intensified the demand for electronic hardware with low energy consumption and fast computing speed. Neuromorphic photonics have emerged as a viable alternative to process high-throughput information at the physical space. However, the simultaneous attainment of high linear and nonlinear expressivity poses a considerable challenge due to the power efficiency and impaired manipulability in conventional nonlinear materials and optoelectronic conversion. Here, we introduce a parallel nonlinear neuromorphic processor that enables arbitrary superposition of information states in multidimensional channels, only by leveraging the temporal encoding of spatiotemporal metasurfaces. We experimentally demonstrated the concept based on distributed spatiotemporal metasurfaces, showcasing robust performance in multilabel recognition and multitask parallelism with asynchronous modulation. Our nonlinear processor demonstrates dynamic memory capability in real-time responsiveness to canonical maze-solving problem. Our work opens up a flexible avenue for a variety of temporally modulated neuromorphic processors tailored for complex scenarios.