Cargando…
Design of a 2-Bit Neural Network Quantizer for Laplacian Source
Achieving real-time inference is one of the major issues in contemporary neural network applications, as complex algorithms are frequently being deployed to mobile devices that have constrained storage and computing power. Moving from a full-precision neural network model to a lower representation b...
Autores principales: | Perić, Zoran, Savić, Milan, Simić, Nikola, Denić, Bojan, Despotović, Vladimir |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8393619/ https://www.ncbi.nlm.nih.gov/pubmed/34441074 http://dx.doi.org/10.3390/e23080933 |
Ejemplares similares
-
Whether the Support Region of Three-Bit Uniform Quantizer Has a Strong Impact on Post-Training Quantization for MNIST Dataset?
por: Nikolić, Jelena, et al.
Publicado: (2021) -
Speaker Recognition Using Constrained Convolutional Neural Networks in Emotional Speech
por: Simić, Nikola, et al.
Publicado: (2022) -
How Many Bits Does it Take to Quantize Your Neural Network?
por: Giacobbe, Mirco, et al.
Publicado: (2020) -
A Novel Low-Bit Quantization Strategy for Compressing Deep Neural Networks
por: Long, Xin, et al.
Publicado: (2020) -
The Laplacian spectrum of neural networks
por: de Lange, Siemon C., et al.
Publicado: (2014)