Deep Quantization of Graph Neural Networks with Run-Time Hardware-Aware Training.
Olle HanssonMahdieh GrailooOscar GustafssonJosé L. Núñez-YáñezPublished in: ARC (2024)
Keyphrases
- neural network
- training process
- training algorithm
- back propagation
- backpropagation algorithm
- feedforward neural networks
- feed forward neural networks
- artificial neural networks
- low cost
- real time
- hardware and software
- multi layer perceptron
- pattern recognition
- neural network training
- graph matching
- directed graph
- connected components
- graph structure
- graph model
- error back propagation
- graph theoretic
- graph representation
- fuzzy logic
- random walk
- weighted graph
- graph theory
- genetic algorithm
- feed forward
- deep architectures
- training examples
- computational complexity
- fault diagnosis
- training set
- recurrent networks
- radial basis function network
- image processing
- graphical models
- massively parallel
- wavelet transform
- directed acyclic graph
- training samples
- embedded systems
- multilayer perceptron
- test set
- neural network model
- structured data