Traditional von Neumann computing systems involve separate processing and memory
units. However, data movement is costly in terms of time and energy and this problem is …

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

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 …

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

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 …

Carbon nanotube (CNT) devices and electronics are achieving maturity and directly
competing or surpassing devices that use conventional materials. CNTs have demonstrated …

Content addressable memory (CAM) is widely used in associative search tasks due to its
parallel pattern matching capability. As more complex and data‐intensive tasks emerge, it is …

Brain-inspired hyperdimensional (HD) computing models neural activity patterns of the very
size of the brain's circuits with points of a hyperdimensional space, that is, with hypervectors …

The rapid growth of the Internet of Things (IoTs) has resulted in an explosive increase in
data, and thus has raised new challenges for data processing units. Edge computing, which …

The development of high-performance p-type oxides with high hole mobility and a wide
bandgap is critical for the applications of metal oxide semiconductors in vertically integrated …