A Theoretical Perspective on Hyperdimensional Computing

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.

Authors

• Anthony Thomas a:1:{s:5:"en_US";s:12:"UC San Diego";}
• Sanjoy Dasgupta
• Tajana Rosing University of California San Diego

Keywords

• Knowledge Representation
• mathematical foundations