Brain-inspired Hyperdimensional Computing (HDC) is an emerging framework in low-
energy designs for solving classification tasks at the edge. Unlike mainstream neural …

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 …

Federated Learning (FL) is a widely adopted distributed learning paradigm for to its privacy-
preserving and collaborative nature. In FL, each client trains and sends a local model to the …

HD computing is a symbolic representation system which performs various learning tasks in
a highly-parallelizable and binary-centric way by drawing inspiration from concepts in …

Hyperdimensional vector processing is a nascent computing approach that mimics the brain
structure and offers lightweight, robust, and efficient hardware solutions for different learning …

Introducing HyperSense, our co-designed hardware and software system efficiently controls
Analog-to-Digital Converter (ADC) modules' data generation rate based on object presence …

Today's machine learning (ML) systems, running workloads, such as deep neural networks,
which require billions of parameters and many hours to train a model, consume a significant …

In recent years, both online and offline deep learning models have been developed for time
series forecasting. However, offline deep forecasting models fail to adapt effectively to …

Hyperdimensional computing (HDC) is a computational paradigm that leverages the
mathematical properties of high-dimensional vector spaces to manipulate data as symbolic …

In conventional federated hyperdimensional computing (HDC), training larger models
usually results in higher predictive performance but also requires more computational …