A Theoretical Perspective on Hyperdimensional Computing
Abstract
Hyperdimensional (HD) computing is a set of neurally inspired methods for obtaining highdimensional, 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.
Cite
Text
Thomas et al. "A Theoretical Perspective on Hyperdimensional Computing." Journal of Artificial Intelligence Research, 2021. doi:10.1613/JAIR.1.12664Markdown
[Thomas et al. "A Theoretical Perspective on Hyperdimensional Computing." Journal of Artificial Intelligence Research, 2021.](https://mlanthology.org/jair/2021/thomas2021jair-theoretical/) doi:10.1613/JAIR.1.12664BibTeX
@article{thomas2021jair-theoretical,
title = {{A Theoretical Perspective on Hyperdimensional Computing}},
author = {Thomas, Anthony and Dasgupta, Sanjoy and Rosing, Tajana},
journal = {Journal of Artificial Intelligence Research},
year = {2021},
pages = {215-249},
doi = {10.1613/JAIR.1.12664},
volume = {72},
url = {https://mlanthology.org/jair/2021/thomas2021jair-theoretical/}
}