Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task.

Forno, Evelina; Fra, Vittorio; Pignari, Riccardo; Macii, Enrico; Urgese, Gianvito

Forno, Evelina; Fra, Vittorio; Pignari, Riccardo; Macii, Enrico; Urgese, Gianvito.

Afiliación

Forno E; Politecnico di Torino, Electronic Design Automation (EDA) Group, Turin, Italy.
Fra V; Politecnico di Torino, Electronic Design Automation (EDA) Group, Turin, Italy.
Pignari R; Politecnico di Torino, Electronic Design Automation (EDA) Group, Turin, Italy.
Macii E; Politecnico di Torino, Electronic Design Automation (EDA) Group, Turin, Italy.
Urgese G; Politecnico di Torino, Electronic Design Automation (EDA) Group, Turin, Italy.

Front Neurosci ; 16: 999029, 2022.

Article en En | MEDLINE | ID: mdl-36620463

Palabras clave

IoT applications; Spiking Neural Network; benchmarking; event based encoding; neuromorphic computing; neuromorphic encoding techniques; spatio-temporal pattern recognition; time-varing signals

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Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: Front Neurosci Año: 2022 Tipo del documento: Article País de afiliación: Italia