Models and algorithms for implementing energy-efficient spiking neural networks on neuromorphic hardware at the edge

Manon Dampfhoffer

Open source

DOI: 10.70675/dec25346zd9ddz4b65zaeb0z54e7d1df8a60
Published: Not recorded
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Publisher: Agence Bibliographique de l'Enseignement Supérieur
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Cite this work

BibTeX

@article{allodium:10.70675/dec25346zd9ddz4b65zaeb0z54e7d1df8a60,
  title = {Models and algorithms for implementing energy-efficient spiking neural networks on neuromorphic hardware at the edge},
  author = {Manon Dampfhoffer},
  doi = {10.70675/dec25346zd9ddz4b65zaeb0z54e7d1df8a60},
  url = {https://doi.org/10.70675/dec25346zd9ddz4b65zaeb0z54e7d1df8a60}
}

RIS

TY  - JOUR
TI  - Models and algorithms for implementing energy-efficient spiking neural networks on neuromorphic hardware at the edge
AU  - Manon Dampfhoffer
DO  - 10.70675/dec25346zd9ddz4b65zaeb0z54e7d1df8a60
UR  - https://doi.org/10.70675/dec25346zd9ddz4b65zaeb0z54e7d1df8a60
ER  -

APA

Dampfhoffer, M. (n.d.). Models and algorithms for implementing energy-efficient spiking neural networks on neuromorphic hardware at the edge. https://doi.org/10.70675/dec25346zd9ddz4b65zaeb0z54e7d1df8a60

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crossref · retrieved 2026-09-26T00:20:50.635Z