基于散斑的光学储层计算机用于时域波形分类
Classification of time-domain waveforms using a speckle-based optical reservoir computer
摘要：
水库计算是一种循环机器学习框架，通过将输入信号映射到更高维的水库空间来扩展问题的维度，该水库空间可以捕获和预测复杂的非线性时间动态特征。在这里，我们报告的散装光学演示的模拟水库计算机使用散斑传播的激光束调制的空间光调制器通过多模波导产生。我们证明，硬件可以成功地执行多变量音频分类任务，使用日本元音扬声器公共数据集。我们执行全波光学计算的这种架构中实现的芯片级平台，使用SiO2波导，并证明它执行以及一个完全数值实现的水库计算。由于实验中使用的所有光学元件都可以使用商业光子集成电路代工厂制造，因此我们的结果展示了一个用于构建可扩展的，芯片级的，能够执行光信号处理的水库计算机的框架。
英文摘要：
Reservoir computing is a recurrent machine learning framework that expands the dimensionality of a problem by mapping an input signal into a higher-dimension reservoir space that can capture and predict features of complex, non-linear temporal dynamics. Here, we report on a bulk optical demonstration of an analog reservoir computer using speckles generated by propagating a laser beam modulated with a spatial light modulator through a multimode waveguide. We demonstrate that the hardware can successfully perform a multivariate audio classification task performed using the Japanese vowel speakers public data set. We perform full wave optical calculations of this architecture implemented in a chip-scale platform using an SiO2 waveguide and demonstrate that it performs as well as a fully numerical implementation of reservoir computing. As all the optical components used in the experiment can be fabricated using a commercial photonic integrated circuit foundry, our result demonstrates a framework for building a scalable, chip-scale, reservoir computer capable of performing optical signal processing.
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