Nonlinear Schrödinger Kernel for Hardware Acceleration of Machine Learning

Alternative machine learning approaches that have extremely low latency and can work with only a small training dataset are needed for applications where the insatiable demands of deep learning methods for computing power and large training data cannot be met. Here we report a new optical accelerator for AI that exploits femtosecond pulses for both data acquisition and computing enabling classification at short time scales for fast optical imaging, sensing, and metrology without increasing data dimensions. Modulation of data onto the spectrum of femtosecond optical pulses followed by projection into a new space using nonlinear optics reduces the latency in the nonlinear classification of certain data by orders of magnitude. This Femtocomputing approach is validated by the classification of various datasets, including brain intracranial pressure, cancer cell imaging, spoken digit recognition, and the classic exclusive OR benchmark for nonlinear operation. The concept is demonstrated by seeding the nonlinear effect that is responsible for many fascinating natural phenomena, such as optical rogue waves. Stimulation of nonlinear optical interactions with spectrally modulated data transforms the data such that a computationally-light linear algorithm can learn a nonlinear decision boundary that separates the data into the correct classes. Since the optical kernel is not trained, its performance is inevitably data-dependent. Quantitative comparison with a popular numerical kernel offers insights into how this physical technique accelerates inference. Single-shot operation is demonstrated using time stretch data acquisition.