Scalable Optical Learning Operator
This work addresses the energy consumption and speed limitations of current electronic processors for machine learning tasks, which is a significant problem for the broader AI community.
This paper introduces an optical computing framework utilizing spatiotemporal effects in multimode fibers to perform various learning tasks, including COVID-19 X-ray image classification, speech recognition, and age prediction from face images. The framework achieves accuracy comparable to digital implementations while addressing the energy scaling problem.
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 one of the powerful means of communicating and processing information and there is intense current interest in optical information processing for realizing high-speed computations. Here we present and experimentally demonstrate an optical computing framework based on spatiotemporal effects in multimode fibers for a range of learning tasks from classifying COVID-19 X-ray lung images and speech recognition to predicting age from face images. The presented framework overcomes the energy scaling problem of existing systems without compromising speed. We leveraged simultaneous, linear, and nonlinear interaction of spatial modes as a computation engine. We numerically and experimentally showed the ability of the method to execute several different tasks with accuracy comparable to a digital implementation.