Titre :

Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition

Auteur(s) :

Garg, N. [Auteur]
Birla Institute of Technology and Science [BITS Pilani]
Laboratoire Nanotechnologies et Nanosystèmes [Sherbrooke] [LN2]
Institut Interdisciplinaire d'Innovation Technologique [Sherbrooke] [3IT]
Balafrej, I. [Auteur]
Institut Interdisciplinaire d'Innovation Technologique [Sherbrooke] [3IT]
Beilliard, Y. [Auteur]
Laboratoire Nanotechnologies et Nanosystèmes [Sherbrooke] [LN2]
Institut Interdisciplinaire d'Innovation Technologique [Sherbrooke] [3IT]
Drouin, Dominique [Auteur]
Institut Interdisciplinaire d'Innovation Technologique [Sherbrooke] [3IT]
Laboratoire Nanotechnologies et Nanosystèmes [Sherbrooke] [LN2]
Alibart, Fabien [Auteur]
Nanostructures, nanoComponents & Molecules - IEMN [NCM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Rouat, J. [Auteur]
Institut Interdisciplinaire d'Innovation Technologique [Sherbrooke] [3IT]

Titre de la manifestation scientifique :

2021 International Conference on Neuromorphic Systems, ICONS 2021

Ville :

Online

Pays :

Canada

Date de début de la manifestation scientifique :

2021-07-27

Éditeur :

Association for Computing Machinery

Date de publication :

2021

Mot(s)-clé(s) en anglais :

Artificial Intelligence
EMG
Event driven computing
Neuromorphic computing
Reservoir computing
Spiking neurons

Discipline(s) HAL :

Sciences de l'ingénieur [physics]

Résumé en anglais : [en]

Surface electromyogram (sEMG) signals result from muscle movement and hence they are an ideal candidate for benchmarking event-driven sensing and computing. We propose a simple yet novel approach for optimizing the spike ...
Lire la suite >Surface electromyogram (sEMG) signals result from muscle movement and hence they are an ideal candidate for benchmarking event-driven sensing and computing. We propose a simple yet novel approach for optimizing the spike encoding algorithm's hyper-parameters inspired by the readout layer concept in reservoir computing. Using a simple machine learning algorithm after spike encoding, we report performance higher than the state-of-The-Art spiking neural networks on two open-source datasets for hand gesture recognition. The spike encoded data is processed through a spiking reservoir with a biologically inspired topology and neuron model. When trained with the unsupervised activity regulation CRITICAL algorithm to operate at the edge of chaos, the reservoir yields better performance than state-of-The-Art convolutional neural networks. The reservoir performance with regulated activity was found to be 89.72% for the Roshambo EMG dataset and 70.6% for the EMG subset of sensor fusion dataset. Therefore, the biologically-inspired computing paradigm, which is known for being power efficient, also proves to have a great potential when compared with conventional AI algorithms. © 2021 Owner/Author.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Projet ANR :

Unsupervised spiking neural networks with analog memristive devices for edge computing

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL