Supernetwork-based efficient mapping of deep learning applications to mixed-precision hardware using model adaptation.

Benmeziane H, Lammie C, Boybat I, Rasch M, Le Gallo M, Vasilopoulos A, Tsai H, Burr GW, Narayanan V, El Maghraoui K, Sebastian A

Open source

DOI
10.1038/s41467-026-71071-1
Published
2026 Mar 27
Container
Nature communications
Publisher
Not recorded
Open access
yes

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BibTeX

@article{allodium:10.1038/s41467-026-71071-1,
  title = {Supernetwork-based efficient mapping of deep learning applications to mixed-precision hardware using model adaptation.},
  author = {Benmeziane H and Lammie C and Boybat I and Rasch M and Le Gallo M and Vasilopoulos A and Tsai H and Burr GW and Narayanan V and El Maghraoui K and Sebastian A},
  year = {2026},
  journal = {Nature communications},
  doi = {10.1038/s41467-026-71071-1},
  url = {https://doi.org/10.1038/s41467-026-71071-1}
}

RIS

TY  - JOUR
TI  - Supernetwork-based efficient mapping of deep learning applications to mixed-precision hardware using model adaptation.
AU  - Benmeziane H
AU  - Lammie C
AU  - Boybat I
AU  - Rasch M
AU  - Le Gallo M
AU  - Vasilopoulos A
AU  - Tsai H
AU  - Burr GW
AU  - Narayanan V
AU  - El Maghraoui K
AU  - Sebastian A
PY  - 2026
JO  - Nature communications
DO  - 10.1038/s41467-026-71071-1
UR  - https://doi.org/10.1038/s41467-026-71071-1
ER  -

APA

H, B., C, L., I, B., M, R., M, L. G., A, V., H, T., GW, B., V, N., K, E. M., & A, S. (2026). Supernetwork-based efficient mapping of deep learning applications to mixed-precision hardware using model adaptation.. Nature communications. https://doi.org/10.1038/s41467-026-71071-1

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