Hyperdimensional computing (HDC) is an emerging computing paradigm that imitates the
brain's structure to offer a powerful and efficient processing and learning model. In HDC, the …

Brain-inspired hyperdimensional computing (HDC), also known as vector symbolic
architecture (VSA), is an emerging “non-von Neumann” computing scheme that imitates …

Hyperdimensional computing (HDC) is a paradigm for data representation and learning
originating in computational neuroscience. HDC represents data as high-dimensional, low …

Hyperdimensional computing (HD), also known as vector symbolic architectures (VSA), is an
emerging and promising paradigm for cognitive computing. At its core, HD/VSA is …

Hyperdimensional computing (HDC) is emerging as an efficient and robust computing
paradigm that has strong resilience to various types of errors. The robustness of HDC makes …

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

Hyperdimensional computing (HDC) is a computational paradigm that leverages the
mathematical properties of high-dimensional vector spaces to manipulate data as symbolic …

As a new computing paradigm, hyperdimensional computing (HDC) has gradually
manifested its advantages in edge-side intelligent applications by virtue of its interpretability …

Federated learning (FL) enables a loose set of participating clients to collaboratively learn a
global model via coordination by a central server and with no need for data sharing. Existing …

Brain-inspired hyperdimensional computing (HDC) is a new computational paradigm that
encodes input sample into a hypervector (generally with dimensions of), and performs …