Streamlined photonic reservoir computer with augmented memory capabilities
具有增强存储能力的流线型光子储层计算机
ストレージ機能を強化した流線型フォトニックレイヤコンピュータ
향상된 스토리지 기능을 갖춘 유선형 광 스토리지 계층 컴퓨터
Computadora de depósito fotónico aerodinámico con capacidad de almacenamiento mejorada
Ordinateur à réservoir photonique simplifié avec capacité de stockage améliorée
Упрощенный фотонический резервуарный компьютер с расширенными возможностями памяти
Changdi Zhou ¹ ², Yu Huang ¹ ², Yigong Yang ¹ ², Deyu Cai ¹ ², Pei Zhou ¹ ², Kuenyao Lau ¹ ², Nianqiang Li ¹ ², Xiaofeng Li ¹ ²
¹ School of Optoelectronic Science and Engineering & Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University, Suzhou 215006, China
中国 苏州 苏州大学光电科学与工程学院 苏州纳米科技协同创新中心
² Key Lab of Advanced Optical Manufacturing Technologies of Jiangsu Province & Key Lab of Modern Optical Technologies of Education Ministry of China, Soochow University, Suzhou 215006, China
中国 苏州 苏州大学 江苏省先进光学制造技术重点实验室 教育部现代光学技术重点实验室
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.
