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Floating Point and Fixed Point 32-bits Quantizers for Quantization of Weights of Neural Networks
| Title: | Floating Point and Fixed Point 32-bits Quantizers for Quantization of Weights of Neural Networks | Authors: | Peric, Zoran   ; Savic, Milan ; Dincic, Milan ; Vucic, Nikola ; Djosic, Danijel ; Milosavljevic, Srdjan |
Issue Date: | 2021 | Publication: | Proceedings: 12th International Symposium on Advanced Topics in Electrical Engineering (ATEE). March 25–27, 2021, Bucharest, Romania | ISSN: | 1843-8571  Search Idenfier |
Publisher: | Bucharest: Faculty of Electrical Engineering, University POLITEHNICA of Bucharest, Romania | Type: | Conference Paper | DOI: | 10.1109/atee52255.2021.9425265 | WoS-ID: | 000676164800120 | Scopus-ID: | 2-s2.0-85106720063 | URI: | https://enauka.gov.rs/handle/123456789/781667 | URL: | https://www.researchgate.net/publication/351533582_Floating_Point_and_Fixed_Point_32-bits_Quantizers_for_Quantization_of_Weights_of_Neural_Networks | Project: | Ministry of Education, Science and Technological Development, Serbia Science Fund of the Republic of Serbia [6527104] (AI-Com-in-AI) |
Metadata source: | (Preuzeto iz ORCID-a) Djosic, Danijel | Note: | Пуни текст није јавно доступан; приступ је могућ уз куповину/претплату или на захтев ауторима. | Availability note: | Пуни текст није јавно доступан | M-category: | Mp. category will be shown later |
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