Memorization is an essential functionality that enables today's machine learning algorithms
to provide a high quality of learning and reasoning for each prediction. Memorization gives …

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 …

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 …

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

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 …

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 …

Machine learning (ML) algorithms are key enablers to effectively assimilate and extract
information from many generated data in the Internet of Things. However, running ML …

In a number of machine learning models, an input query is searched across the trained class
vectors to find the closest feature class vector in cosine similarity metric. However …