A Theoretical Perspective on Hyperdimensional Computing
Anthony Thomas, Sanjoy Dasgupta, Tajana Rosing
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.
Topics & Concepts
Computer sciencePerspective (graphical)Latency (audio)Focus (optics)Set (abstract data type)Variety (cybernetics)Theoretical computer scienceArtificial intelligenceComputer engineeringData scienceProgramming languageTelecommunicationsPhysicsOpticsFerroelectric and Negative Capacitance DevicesAdvanced Memory and Neural ComputingNeural Networks and Reservoir Computing