题目：Streamlined photonic reservoir computer with augmented memory capabilities

作者：Changdi Zhou1,2, Yu Huang1,2, Yigong Yang1,2, Deyu Cai1,2, Pei Zhou1,2, Kuenyao Lau1,2, Nianqiang Li1,2* and Xiaofeng Li1,2*

单位：

1School of Optoelectronic Science and Engineering & Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University, Suzhou 215006, China

2Key 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

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.

摘要：光子平台正逐步成为应对人工智能领域持续增长需求的重要解决方案，其中光子时延型储备池计算（TDRC）技术备受瞩目。然而，该计算范式虽能仅采用单一光子器件作为非线性节点进行数据处理，但其性能高度依赖于延迟反馈回路（FL）所提供的渐退记忆能力，这极大限制了物理实现的扩展性，尤其是在高度集成化芯片领域。在此，我们提出了一种基于类卷积编码（QC）的简化型光子架构，通过设计的编码方式实现了更灵活的参数配置，彻底摆脱了对FL的依赖。与传统TDRC不同，基于类卷积编码的储备池计算（QRC）通过编码数据实现了时间相关特征的提取，从而使系统获得增强的记忆能力。因此，我们所提出的QRC方案无需构建FL即可高效处理时序相关任务或序列数据。此外，该硬件系统可采用低功耗、易集成的垂直腔面发射激光器实现高性能并行处理。我们通过仿真与实验对比验证了QRC与TDRC的性能表现，结果表明，结构更简单的QRC在多项基准任务中均展现出优越性。我们的研究结果可能为深度神经网络的硬件实现提供一条极具前景的技术路径。

影响因子：15.3

链接：//doi.org/10.29026/oea.2025.240135