I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
Energy Efficient Implementation of Machine Learning Algorit..:
, In:
2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS)
,
Osta, Mario
;
Alameh, Mohamad
;
Younes, Hamoud
.. - p. 21-24 , 2019
Link:
https://doi.org/10.1109/ICECS46596.2019.8965157
RT T1
2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS)
: T1
Energy Efficient Implementation of Machine Learning Algorithms on Hardware Platforms
UL https://suche.suub.uni-bremen.de/peid=ieee-8965157&Exemplar=1&LAN=DE A1 Osta, Mario A1 Alameh, Mohamad A1 Younes, Hamoud A1 Ibrahim, Ali A1 Valle, Maurizio YR 2019 K1 Embedded Machine Learning K1 Low Power platforms K1 Energy Efficient techniques K1 Deep Learning SP 21 OP 24 LK http://dx.doi.org/https://doi.org/10.1109/ICECS46596.2019.8965157 DO https://doi.org/10.1109/ICECS46596.2019.8965157 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)