"VEDLIoT: Very Efficient Deep Learning in IoT."

Martin Kaiser et al. (2022)

> Home

Details and statistics

DOI: 10.48550/ARXIV.2207.00675

access: open

type: Informal or Other Publication

metadata version: 2024-02-06

- view
  - electronic edition via DOI (open access)
  authority control:
- export record
  dblp key:
  - journals/corr/abs-2207-00675
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/journals/corr/abs-2207-00675
Martin Kaiser, René Griessl, Nils Kucza, Carola Haumann, Lennart Tigges, Kevin Mika, Jens Hagemeyer, Florian Porrmann, Ulrich Rückert, Micha vor dem Berge, Stefan Krupop, Mario Porrmann, Marco Tassemeier, Pedro Trancoso, Fareed Qararyah, Stavroula Zouzoula, António Casimiro, Alysson Bessani, José Cecílio, Stefan Andersson, Oliver Brunnegård, Olof Eriksson, Roland Weiss, Franz Meierhöfer, Hans Salomonsson, Elaheh Malekzadeh, Daniel Ödman, Anum Khurshid, Pascal Felber, Marcelo Pasin, Valerio Schiavoni, Jämes Ménétrey, Karol Gugula, Piotr Zierhoffer, Eric Knauss, Hans-Martin Heyn:
VEDLIoT: Very Efficient Deep Learning in IoT. CoRR abs/2207.00675 (2022)

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.