Teğin Uğur, Yıldırım Mustafa, Oğuz İlker, Moser Christophe, Psaltis Demetri
Optics Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Laboratory of Applied Photonics Devices, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Nat Comput Sci. 2021 Aug;1(8):542-549. doi: 10.1038/s43588-021-00112-0. Epub 2021 Aug 20.
Today's heavy machine learning tasks are fueled by large datasets. Computing is performed with power-hungry processors whose performance is ultimately limited by the data transfer to and from memory. Optics is a powerful means of communicating and processing information, and there is currently intense interest in optical information processing for realizing high-speed computations. Here we present and experimentally demonstrate an optical computing framework called scalable optical learning operator, which is based on spatiotemporal effects in multimode fibers for a range of learning tasks including classifying COVID-19 X-ray lung images, speech recognition and predicting age from images of faces. The presented framework addresses the energy scaling problem of existing systems without compromising speed. We leverage simultaneous, linear and nonlinear interaction of spatial modes as a computation engine. We numerically and experimentally show the ability of the method to execute several different tasks with accuracy comparable with a digital implementation.
如今，大型数据集推动了繁重的机器学习任务。计算由耗电量大的处理器执行，其性能最终受限于与内存之间的数据传输。光学是一种强大的信息通信和处理手段，目前人们对用于实现高速计算的光学信息处理有着浓厚兴趣。在此，我们提出并通过实验演示了一种名为可扩展光学学习算子的光学计算框架，该框架基于多模光纤中的时空效应，可用于一系列学习任务，包括对新冠肺炎X射线肺部图像进行分类、语音识别以及从面部图像预测年龄。所提出的框架在不影响速度的情况下解决了现有系统的能量缩放问题。我们利用空间模式的同时、线性和非线性相互作用作为计算引擎。我们通过数值和实验表明，该方法能够以与数字实现相当的精度执行多种不同任务。
