Workload prediction for adaptive power scaling using deep learning
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Year
: 2014DOI: 10.1109/OCEANS.2014.7003300
Keyword(s): Fuels,Marine vehicles,Monitoring,Ports (Computers),Sea measurements,Sea state,Software
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Workload prediction for adaptive power scaling using deep learning
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| contributor author | Tarsa, S.J. , Kumar, A.P. , Kung, H.T. | |
| date accessioned | 2020-03-12T19:54:02Z | |
| date available | 2020-03-12T19:54:02Z | |
| date issued | 2014 | |
| identifier other | 6838580.pdf | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/994396?locale-attribute=en | |
| format | general | |
| language | English | |
| publisher | IEEE | |
| title | Workload prediction for adaptive power scaling using deep learning | |
| type | Conference Paper | |
| contenttype | Metadata Only | |
| identifier padid | 8113801 | |
| subject keywords | Fuels | |
| subject keywords | Marine vehicles | |
| subject keywords | Monitoring | |
| subject keywords | Ports (Computers) | |
| subject keywords | Sea measurements | |
| subject keywords | Sea state | |
| subject keywords | Software | |
| identifier doi | 10.1109/OCEANS.2014.7003300 | |
| journal title | C Design & Technology (ICICDT), 2014 IEEE International Conference on | |
| filesize | 944652 | |
| citations | 0 |