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

Hyper-Dimensional computing (HDC) is a brain-inspired learning approach for efficient and
robust learning on today's embedded devices. HDC supports single-pass learning, where it …

Today's applications are using machine learning algorithms to analyze the data collected
from a swarm of devices on the Internet of Things (IoT). However, most existing learning …

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 …

The von Neumann bottleneck is imposed due to the explosion of data transfers and
emerging data-intensive applications in heterogeneous system architectures. The …

Hyperdimensional computing (HDC) is an emerging learning paradigm that computes with
high dimensional binary vectors. There is an active line of research on HDC in the …

Today's systems rely on sending all the data to the cloud and then using complex
algorithms, such as Deep Neural Networks, which require billions of parameters and many …

Training deep neural network (DNN) models is a resource-intensive, iterative process. For
this reason, nowadays, complex optimizers like Adam are widely adopted as it increases the …

Hyper-Dimensional (HD) computing emulates the human short memory functionality by
computing with hyper-vectors as an alternative to computing with numbers. The main goal of …

Computational storage adds computing to storage devices, providing potential benefits in
offload, data-reduction, and lower energy. Successful computational SSD architectures …