Hyperdimensional (HD) computing is built upon its unique data type referred to as
hypervectors. The dimension of these hypervectors is typically in the range of tens of …

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

In the Internet of Things (IoT) domain, many applications are running machine learning
algorithms to assimilate the data collected in the swarm of devices. Sending all data to the …

Today's applications generate a large amount of data that need to be processed by learning
algorithms. In practice, the majority of the data are not associated with any labels …

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 (HD) computing models cognition by exploiting properties
of high dimensional statistics-high-dimensional vectors, instead of working with numeric …

Brain-inspired computing models have shown great potential to outperform today's deep
learning solutions in terms of robustness and energy efficiency. Particularly, Hyper …

Motivated by recent innovations in biologically inspired neuromorphic hardware, this article
presents a novel unsupervised machine learning algorithm named Hyperseed that draws on …

Brain-inspired Hyperdimensional Computing (HDC) is an emerging framework in low-
energy designs for solving classification tasks at the edge. Unlike mainstream neural …

Hyperdimensional computing (HD) is a new brain-inspired algorithm that mimics the human
brain for cognitive tasks. Despite its inherent potential, the practical efficiency of HD is tied to …