We propose a unified machine learning (ML) framework for simultaneously performing
electrical load forecasting and unsupervised anomaly detection in real time. For load …

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 separation of computing units and memory in the computer architecture mandates
energy-intensive data transfers creating the von Neumann bottleneck. This bottleneck is …

Brain-inspired hyperdimensional computing (HDC) is an emerging computational paradigm
that mimics brain cognition and leverages hyperdimensional vectors with fully distributed …

Load anomalies in power distribution systems are relatively rare, yielding imbalanced
classification datasets. Consequently, traditional artificial intelligence approaches for …

Inspired by the way human brain works, the emerging hyperdimensional computing (HDC)
is getting more and more attention. HDC is an emerging computing scheme based on the …

Brain-inspired hyperdimensional computing (HDC), also known as vector symbolic
architecture (VSA), is an emerging “non-von Neumann” computing scheme that imitates …

Hyperdimensional computing (HDC) is an emerging computational framework inspired by
the brain that operates on vectors with thousands of dimensions to emulate cognition. Unlike …

In this work, we present ODHD, an algorithm for outlier detection based on
hyperdimensional computing (HDC), a non-classical learning paradigm. Along with the HDC …

While machine learning (ML) methods especially deep neural networks (DNNs) promise
enormous societal and economic benefits, their deployments present daunting challenges …