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20172023
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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Showing 2018Show all

7 papers · 1 filter

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…

cs.ET2018

Hyperdimensional Computing Nanosystem

Abbas Rahimi, Tony F. Wu, Haitong Li +4

One viable solution for continuous reduction in energy-per-operation is to rethink functionality to cope with uncertainty by adopting computational approaches that are inherently r…

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…

cs.ET2018

Hardware Optimizations of Dense Binary Hyperdimensional Computing: Rematerialization of Hypervectors, Binarized Bundling, and Combinational Associative Memory

Manuel Schmuck, Luca Benini, Abbas Rahimi

Brain-inspired hyperdimensional (HD) computing models neural activity patterns of the very size of the brain's circuits with points of a hyperdimensional space, that is, with hyper…

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…