  The recent rapid increase in demand for data processing has resulted in the
need for novel machine learning concepts and hardware. Physical reservoir
computing and an extreme learning machine are novel computing paradigms based
on physical systems themselves, where the high dimensionality and nonlinearity
play a crucial role in the information processing. Herein, we propose the use
of multidimensional speckle dynamics in multimode fibers for information
processing, where input information is mapped into the space, frequency, and
time domains by an optical phase modulation technique. The speckle-based
mapping of the input information is high-dimensional and nonlinear and can be
realized at the speed of light; thus, nonlinear time-dependent information
processing can successfully be achieved at fast rates when applying a
reservoir-computing-like-approach. As a proof-of-concept, we experimentally
demonstrate chaotic time-series prediction at input rates of 12.5 Gigasamples
per second. Moreover, we show that owing to the passivity of multimode fibers,
multiple tasks can be simultaneously processed within a single system, i.e.,
multitasking. These results offer a novel approach toward realizing parallel,
high-speed, and large-scale photonic computing.
