"An End-to-End HW/SW Co-Design Methodology to Design Efficient Deep Neural ..."

Michael J. Klaiber et al. (2019)

> Home

Details and statistics

DOI: 10.1145/3372394.3372396

access: closed

type: Conference or Workshop Paper

metadata version: 2022-10-02

- view
  - electronic edition via DOI
  - unpaywalled version
  authority control:
- export record
  dblp key:
  - conf/intesa/KlaiberVAKEBGF19
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/conf/intesa/KlaiberVAKEBGF19
Michael J. Klaiber, Sebastian Vogel, Axel Acosta, Robert Korn, Leonardo Ecco, Kristine Back, Andre Guntoro, Ingo Feldner:
An End-to-End HW/SW Co-Design Methodology to Design Efficient Deep Neural Network Systems using Virtual Models. INTESA 2019: 18-22

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.