  Hyperdimensional (HD) computing is a set of neurally inspired methods for
obtaining high-dimensional, low-precision, distributed representations of data.
These representations can be combined with simple, neurally plausible
algorithms to effect a variety of information processing tasks. HD computing
has recently garnered significant interest from the computer hardware community
as an energy-efficient, low-latency, and noise-robust tool for solving learning
problems. In this review, we present a unified treatment of the theoretical
foundations of HD computing with a focus on the suitability of representations
for learning.
