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 …

Advances in bioinformatics are primarily due to new algorithms for processing diverse
biological data sources. While sophisticated alignment algorithms have been pivotal in …

The latest hardware accelerators proposed for graph applications primarily focus on graph
neural networks (GNNs) and graph mining. High-level graph reasoning tasks, such as graph …

This is the first work to present a reliable application for highly scaled (down to merely 3nm),
multi-bit Ferroelectric FET (FeFET) technology. FeFET is one of the up-and-coming …

The biosignals consist of several sensors that collect time series information. Since time
series contain temporal dependencies, they are difficult to process by existing machine …

While Graph Neural Networks (GNNs) have demonstrated remarkable achievements in
knowledge graph reasoning, their computational efficiency on conventional computing …

In this paper, we propose EdgeHD, a hierarchy-aware learning solution that performs online
training and inference in a highly distributed, cost-effective way. We use brain-inspired …

In this paper, we present Hyperdimensional Hybrid Learning (HDHL), which combines
model-free and model-based Reinforcement Learning, to effectively reduce the …

Hyperdimensional Computing (HDC) is a human brain-inspired computing paradigm that
processes neural activity patterns with high dimensional vectors. Existing HDC accelerators …

Integral transforms are invaluable mathematical tools to map functions into spaces where
they are easier to characterize. We introduce the hyperdimensional transform as a new kind …