GraphHD: Efficient graph classification using hyperdimensional computing

  title={GraphHD: Efficient graph classification using hyperdimensional computing},
  author={Igor O. Nunes and Mike Heddes and Tony Givargis and Alexandru Nicolau and Alexander V. Veidenbaum},
  journal={2022 Design, Automation \& Test in Europe Conference \& Exhibition (DATE)},
Hyperdimensional Computing (HDC) developed by Kanerva is a computational model for machine learning inspired by neuroscience. HDC exploits characteristics of biological neural systems such as high-dimensionality, randomness and a holographic representation of information to achieve a good balance between accuracy, efficiency and robustness. HDC models have already been proven to be useful in different learning applications, especially in resource-limited settings such as the increasingly… 

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