摘要：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.
