摘要
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.
基金
National Natural Science Foundation of China(62171305,62405206,62004135,62001317,62111530301)
Natural Science Foundation of Jiangsu Province(BK20240778,BK20241917)
State Key Laboratory of Advanced Optical Communication Systems and Networks,China(2023GZKF08)
China Postdoctoral Science Foundation(2024M752314)
Postdoctoral Fellowship Program of CPSF(GZC20231883)
Innovative and Entrepreneurial Talent Program of Jiangsu Province(JSSCRC2021527).
