Gast ::
Post-Training Quantization for Energy Efficient Realization of Deep Neural Networks
; ;
In
21st IEEE International Conference on Machine Learning and Applications : ICMLA 2022 : 12-14 December 2022, Nassau, the Bahamas : proceedings / editors: M. Arif Wani, Mehmed Kantardzic, Vasile Palade, Daniel Neagu, Longzhi Yang, Kit-Yan Chan ; publisher: IEEE, Seiten/Artikel-Nr: 1559-1566
2022
Online
DOI: 10.1109/ICMLA55696.2022.00243
URL: https://publications.rwth-aachen.de/record/955391/files/955391.pdf
Einrichtungen
Restricted: 
PDF
Dokumenttyp
Contribution to a book/Contribution to a conference proceedings
Format
online, print
Sprache
English
Anmerkung
Peer reviewed article
Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-85152213330
WOS Core Collection: WOS:000980994900233
Interne Identnummern
RWTH-2023-03444
Datensatz-ID: 955391
Beteiligte Länder
Germany
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The record appears in these collections: |
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