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Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case
Deep learning is finding its way into high energy physics by replacing traditional Monte Carlo simulations. However, deep learning still requires an excessive amount of computational resources. A promising approach to make deep learning more efficient is to quantize the parameters of the neural netw...
| Autores principales: | , , , , , , , |
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| Lenguaje: | eng |
| Publicado: |
2021
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.5220/0010245002510258 http://cds.cern.ch/record/2758899 |