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Communication Dans Un Congrès Année : 2021

VS2N : Interactive Dynamic Visualization and Analysis Tool for Spiking Neural Networks

Mohammed Kamel Benhaoua
  • Fonction : Auteur
Philippe Devienne
Pierre Boulet

Résumé

Bio-inspired computing architectures enable ultralow power consumption and massive parallelism using neuromorphic computing, which is apt to implement Spiking Neural Networks (SNN). Such architectures are particularly suitable for energy-constrained applications. A deeper understanding of Spiking Neural Networks (SNN) behavior during training is needed to improve these architectures. This paper presents VS2N, a web-based tool for interactive visualization and analysis of SNN activity over time. This simulator-independent tool offers a way to examine, analyze and validate different hypotheses about SNN activity. We present available analysis modules and use-cases of the tool as an example.
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Dates et versions

hal-03267042 , version 1 (22-06-2021)

Identifiants

  • HAL Id : hal-03267042 , version 1

Citer

Hammouda Elbez, Mohammed Kamel Benhaoua, Philippe Devienne, Pierre Boulet. VS2N : Interactive Dynamic Visualization and Analysis Tool for Spiking Neural Networks. Content-Based Multimedia Indexing, Jun 2021, Lille, France. ⟨hal-03267042⟩
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