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.
@article{arxiv.2010.07426,
title = {A Theoretical Perspective on Hyperdimensional Computing},
author = {Anthony Thomas and Sanjoy Dasgupta and Tajana Rosing},
journal= {arXiv preprint arXiv:2010.07426},
year = {2022}
}