Abstract

Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence, among which photonic time-delay reservoir computing (TDRC) is widely anticipated. While such a computing paradigm can only employ a single photonic device as the nonlinear node for data processing, the performance highly relies on the fading memory provided by the delay feedback loop (FL), which sets a restriction on the extensibility of physical implementation, especially for highly integrated chips.

Here, we present a simplified photonic scheme for more flexible parameter configurations leveraging the designed quasi-convolution coding (QC), which completely gets rid of the dependence on FL. Unlike delay-based TDRC, encoded data in QC-based RC (QRC) enables temporal feature extraction, facilitating augmented memory capabilities. Thus, our proposed QRC is enabled to deal with time-related tasks or sequential data without the implementation of FL.

Furthermore, we can implement this hardware with a low-power, easily integrable vertical-cavity surface-emitting laser for high-performance parallel processing. We illustrate the concept validation through simulation and experimental comparison of QRC and TDRC, wherein the simpler-structured QRC outperforms across various benchmark tasks. Our results may underscore an auspicious solution for the hardware implementation of deep neural networks.