This two-part comprehensive survey is devoted to a computing framework most commonly
known under the names Hyperdimensional Computing and Vector Symbolic Architectures …

This is Part II of the two-part comprehensive survey devoted to a computing framework most
commonly known under the names Hyperdimensional Computing and Vector Symbolic …

This article reviews recent progress in the development of the computing framework vector
symbolic architectures (VSA)(also known as hyperdimensional computing). This framework …

Hyperdimensional (HD) computing is a set of neurally inspired methods for obtaining
highdimensional, low-precision, distributed representations of data. These representations …

Vector space models for symbolic processing that encode symbols by random vectors have
been proposed in cognitive science and connectionist communities under the names Vector …

Brain-inspired architectures are gaining increased attention, especially for edge devices to
perform cognitive tasks utilizing its limited energy budget and computing resources …

While neural networks are highly effective at learning task-relevant representations from
data, they typically do not learn representations with the kind of symbolic structure that is …

We introduce a novel encoding scheme for hyperdimensional computing (HDC) image
classification tasks that takes advantage of both spatial awareness of pixels and nonlinear …

Introduction Hyperdimensional Computing (HDC) is a brain-inspired and lightweight
machine learning method. It has received significant attention in the literature as a candidate …

We propose a cognitively plausible method for representingand querying spatial
relationships in a neural architecture. Thistechnique employs a fractional binding operator …