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20172022
most citedEnergy Efficient In-memory Hyperdimensional Encoding for Spatio-temporal Signal Processing

21 citations · 41 across the 5 of their papers we have counts for

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6 papers · 1 filter

eess.SP2021

A 5 μW Standard Cell Memory-based Configurable Hyperdimensional Computing Accelerator for Always-on Smart Sensing

Manuel Eggimann, Abbas Rahimi, Luca Benini

Hyperdimensional computing (HDC) is a brain-inspired computing paradigm based on high-dimensional holistic representations of vectors. It recently gained attention for embedded sma…

eess.SP2020

Binarization Methods for Motor-Imagery Brain-Computer Interface Classification

Michael Hersche, Luca Benini, Abbas Rahimi

Successful motor-imagery brain-computer interface (MI-BCI) algorithms either extract a large number of handcrafted features and train a classifier, or combine feature extraction an…

eess.SP201811 cited

Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based Brain-Computer Interfaces

Michael Hersche, José del R. Millán, Luca Benini +1

Key properties of brain-inspired hyperdimensional (HD) computing make it a prime candidate for energy-efficient and fast learning in biosignal processing. The main challenge is how…

eess.SP2018

One-shot Learning for iEEG Seizure Detection Using End-to-end Binary Operations: Local Binary Patterns with Hyperdimensional Computing

Alessio Burrello, Kaspar Schindler, Luca Benini +1

This paper presents an efficient binarized algorithm for both learning and classification of human epileptic seizures from intracranial electroencephalography (iEEG). The algorithm…

eess.SP2018

Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features

Michael Hersche, Tino Rellstab, Pasquale Davide Schiavone +3

Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain-computer interfa…

eess.SP2018

PULP-HD: Accelerating Brain-Inspired High-Dimensional Computing on a Parallel Ultra-Low Power Platform

Fabio Montagna, Abbas Rahimi, Simone Benatti +2

Computing with high-dimensional (HD) vectors, also referred to as , is a brain-inspired alternative to computing with scalars. Key properties of HD computing…