中心博士生张俊峰的工作——High-speed parallel processing with photonic feedforward reservoir computing（基于光子的高速并行前馈储备池计算网络）的相关成果近期被Optics Express期刊接收发表，该工作得到了国家重点研发计划（2019YFB2203700）、国家自然科学基金（T2225023）的部分资助。基于光子的高速储备池计算（RC）在神经形态计算领域引起了广泛关注。然而，现有的储备池层（RL）架构主要依赖于时延反馈回路，并在读出层的实现中使用模数转换器进行离线数字处理，这在速度和性能上存在固有的局限。在本文中，我们提出了一种非反馈方法，利用光学色散引起的脉冲展宽效应来实现全光RL。通过将调制器的乘法与分布反馈激光二极管的脉冲时间积分的求和相结合，我们成功地实现了光电模拟读出层的线性回归操作。我们提出的全模拟的前馈式光子RC（FF-PhRC）系统在混沌信号预测、语音数字识别和图像手写体分类方面得到了实验证明，并且取得了优异的性能。此外，通过波分复用，我们的系统能够完成并行任务，并将处理能力提高到每个波长10 GHz。这项研究突显了FF-PhRC作为实时神经形态计算高性能、高速工具的潜力。
摘要: High-speed photonic reservoir computing (RC) has garnered significant interest in neuromorphic computing. However, existing reservoir layer (RL) architectures mostly rely on time-delayed feedback loops and use analog-to-digital converters for offline digital processing in the implementation of the readout layer, posing inherent limitations on their speed and capabilities. In this paper, we propose a non-feedback method that utilizes the pulse broadening effect induced by optical dispersion to implement a RL. By combining the multiplication of the modulator with the summation of the pulse temporal integration of the distributed feedback-laser diode, we successfully achieve the linear regression operation of the optoelectronic analog readout layer. Our proposed fully-analog feed-forward photonic RC (FF-PhRC) system is experimentally demonstrated to be effective in chaotic signal prediction, spoken digit recognition, and MNIST classification. Additionally, using wavelength-division multiplexing, our system manages to complete parallel tasks and improve processing capability up to 10 GHz per wavelength. The present work highlights the potential of FF-PhRC as a high-performance, high-speed computing tool for real-time neuromorphic computing.