  Physical reservoir computing is a computational paradigm that enables
spatio-temporal pattern recognition to be performed directly in matter. The use
of physical matter leads the way towards energy-efficient devices capable of
solving machine learning problems without having to build a system of millions
of interconnected neurons. We propose a high performance "skyrmion mixture
reservoir" that implements the reservoir computing model with multi-dimensional
inputs. We show that our implementation solves spoken digit classification
tasks at the nanosecond timescale, with an overall model accuracy of 97.4% and
a less that 1% word error rate; the best performance ever reported for
in-materio reservoir computers. Due to the quality of the results and the low
power properties of magnetic texture reservoirs, we argue that skyrmion fabrics
are a compelling candidate for reservoir computing.
