  Reservoir computing is a neural network approach for processing
time-dependent signals that has seen rapid development in recent years.
Physical implementations of the technique using optical reservoirs have
demonstrated remarkable accuracy and processing speed at benchmark tasks.
However, these approaches require an electronic output layer to maintain high
performance, which limits their use in tasks such as time-series prediction,
where the output is fed back into the reservoir. We present here a reservoir
computing scheme that has rapid processing speed both by the reservoir and the
output layer. The reservoir is realized by an autonomous, time-delay, Boolean
network configured on a field-programmable gate array. We investigate the
dynamical properties of the network and observe the fading memory property that
is critical for successful reservoir computing. We demonstrate the utility of
the technique by training a reservoir to learn the short- and long-term
behavior of a chaotic system. We find accuracy comparable to state-of-the-art
software approaches of similar network size, but with a superior real-time
prediction rate up to 160 MHz.
