Cargando…
Quantization-Aware NN Layers with High-throughput FPGA Implementation for Edge AI
Over the past few years, several applications have been extensively exploiting the advantages of deep learning, in particular when using convolutional neural networks (CNNs). The intrinsic flexibility of such models makes them widely adopted in a variety of practical applications, from medical to in...
Autores principales: | Pistellato, Mara, Bergamasco, Filippo, Bigaglia, Gianluca, Gasparetto, Andrea, Albarelli, Andrea, Boschetti, Marco, Passerone, Roberto |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10222267/ https://www.ncbi.nlm.nih.gov/pubmed/37430583 http://dx.doi.org/10.3390/s23104667 |
Ejemplares similares
-
A survey on text classification: Practical perspectives on the Italian language
por: Gasparetto, Andrea, et al.
Publicado: (2022) -
Quantized visual awareness
por: Escobar, W. A.
Publicado: (2013) -
FPGA-Based Hybrid-Type Implementation of Quantized Neural Networks for Remote Sensing Applications
por: Wei, Xin, et al.
Publicado: (2019) -
Pattern Classification Using Quantized Neural Networks for FPGA-Based Low-Power IoT Devices
por: Biswal, Manas Ranjan, et al.
Publicado: (2022) -
Context-Aware Edge-Based AI Models for Wireless Sensor Networks—An Overview
por: Al-Saedi, Ahmed A., et al.
Publicado: (2022)