A Theoretical Perspective on Hyperdimensional Computing

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.

Automated summary

Hyperdimensional computing is a neurally inspired method to obtain high-dimensional, low-precision, distributed data representations. These representations, when combined with simple algorithms, can perform various information processing tasks effectively. This approach has garnered significant interest for its energy-efficient, low-latency, and noise-robust capabilities in solving learning problems.