- case-insensitive prefix search: default
e.g., sig matches "SIGIR" as well as "signal"
- exact word search: append dollar sign ($) to word
e.g., graph$ matches "graph", but not "graphics"
- boolean and: separate words by space
e.g., codd model
- boolean or: connect words by pipe symbol (|)
Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.
found 6 matches
- Andrew Or, Haoyu Zhang, Michael J. Freedman:
VirtualFlow: Decoupling Deep Learning Model Execution from Underlying Hardware. CoRR abs/2009.09523 (2020)
- Milos Kotlar, Dragan Bojic, Marija Punt, Veljko Milutinovic:
Survey of deployment locations and underlying hardware architectures for contemporary deep neural networks. Int. J. Distributed Sens. Networks 15(8) (2019)
- Georgios P. Katsikas:
NFV Service Chains at the Speed of the Underlying Commodity Hardware. Royal Institute of Technology, Stockholm, Sweden, 2018
- Georgios P. Katsikas, Tom Barbette, Dejan Kostic, Rebecca Steinert, Gerald Q. Maguire Jr.:
Metron: NFV Service Chains at the True Speed of the Underlying Hardware. NSDI 2018: 171-186
- Jiecao Yu, Andrew Lukefahr, David J. Palframan, Ganesh S. Dasika, Reetuparna Das, Scott A. Mahlke:
Scalpel: Customizing DNN Pruning to the Underlying Hardware Parallelism. ISCA 2017: 548-560
- Cagri Balkesen, Jens Teubner, Gustavo Alonso, M. Tamer Özsu:
Main-memory hash joins on multi-core CPUs: Tuning to the underlying hardware. ICDE 2013: 362-373
loading more results
failed to load more results, please try again later
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.