  Today's unrelenting increase in demand for information processing creates the
need for novel computing concepts. Reservoir computing is such a concept that
lends itself particularly well to photonic hardware implementations. Over
recent years, these hardware implementations have gained maturity and now
achieve state-of-the-art performance on several benchmark tasks. However,
implementations so far are essentially all based on sequential data processing,
leaving the inherent parallelism of photonics unexploited. Parallel
implementations process all neurons simultaneously, and therefore have the
potential of reducing computation time by a factor equal to the number of
neurons, compared to sequential architectures. Here, we report a parallel
reservoir computer that uses frequency domain multiplexing of neuron states. We
illustrate its performance on standard benchmark tasks such as nonlinear
channel equalization, the reproduction of a nonlinear 10th-order system, and
speech recognition, obtaining error rates similar to previous optical
experiments. The present experiment is thus an important step towards high
speed, low footprint, all optical photonic information processing.
