Comparative Study of Adaptive Low-Precision Floating-Point and Fixed-Point Quantization for CNNs on FPGA-Oriented Architectures

Authors

Emanuel Trabes (ORCID: https://orcid.org/0000-0003-1294-8985)
Hassene Faiedh (ORCID: https://orcid.org/0000-0002-9033-7365)
Kawthar Dellel
Carlos Valderrama

Institutions

University of Mons (BE)
Higher Institute of Management (RU)
University of Sousse (TN)

Publication Details

Published: 2025-11-20
DOI: https://doi.org/10.1109/adacis65663.2025.11436377
Primary Topic: Model Reduction and Neural Networks
Type: article
Field-Weighted Citation Impact: 0.00

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article

Comparative Study of Adaptive Low-Precision Floating-Point and Fixed-Point Quantization for CNNs on FPGA-Oriented Architectures

Emanuel Trabes, Hassene Faiedh, Kawthar Dellel, Carlos Valderrama

Nov 20, 2025

Model Reduction and Neural Networks

article

Comparative Study of Adaptive Low-Precision Floating-Point and Fixed-Point Quantization for CNNs on FPGA-Oriented Architectures

Emanuel Trabes, Hassene Faiedh, Kawthar Dellel, Carlos Valderrama

November 20, 2025article en

Abstract

No abstract available for this paper.

University of Mons (BE), Higher Institute of Management (RU), University of Sousse (TN)

Affordable and clean energy

Openalex Percentile: Top 24%

Model Reduction and Neural Networks

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Comparative Study of Adaptive Low-Precision Floating-Point and Fixed-Point Quantization for CNNs on FPGA-Oriented Architectures — Emanuel Trabes, Hassene Faiedh, et al. · (2025) | TGRS Research Map | TGRS