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 …

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

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 …

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 …

Pattern matching is one of the key algorithms in identifying and analyzing genomic data. In
this paper, we propose HYPERS, a novel framework supporting highly efficient and parallel …

Brain-inspired Hyperdimensional (HD) computing is a new machine learning approach that
leverages simple and highly parallelizable operations. Unfortunately, none of the published …

Hyperdimensional Computing (HDC) is introduced as a promising solution for robust and
efficient learning on embedded devices with limited resources. Since HDC often runs in a …

Hyperdimensional computing (HDC) has been proposed to more closely model the brain
from the abstract and functionality level. Compared to the traditional sequential regression …

Fully Homomorphic Encryption (FHE) enables arbitrary computations on encrypted data
without decryption, thus protecting data in cloud computing scenarios. However, FHE …

Hyperdimensional Computing (HDC), also known as Vector-Symbolic Architectures (VSA),
is an approach that has been proposed to combine the advantages of distributed vector …