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arXiv · 2010.07426

A Theoretical Perspective on Hyperdimensional Computing

Anthony Thomas·Sanjoy Dasgupta·Tajana Rosing
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Abstract

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.

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Anthony Thomas, Sanjoy Dasgupta, Tajana Rosing. 2020-10-14. A Theoretical Perspective on Hyperdimensional Computing. https://doi.org/10.1613/jair.1.12664

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