UCLA

Time stretch instruments have been exceptionally successful in discovering single-shot ultrafastphenomena such as optical rogue waves and have led to record-speed microscopy, spectroscopy, lidar, etc. These instruments encode the ultrafast events into the spectrum of a femtosecond pulse and then dilate the time scale of the data using group velocity dispersion. Generating as much as Tbit per second of data, they are ideal partners for deep learning networks which by their inherent complexity, require large datasets for training. However, the inference time scale of neural networks in the millisecond regime is orders of magnitude longer than the data acquisition rate of time stretch instruments. This underscores the need to explore means where some of the lower-level computational tasks can be done while the data is still in the optical domain. To address this predicament, we propose the Nonlinear Schr�odinger Kernel computing. This real time computing framework utilizes optical nonlinearities to map the data onto a new domain in which classification accuracy is enhanced, without increasing the data dimensions. A novel training scheme for the kernel is developed by utilizing digital phase encoding of the input data.