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Evaluating Mixed-Precision Arithmetic for 3D Generative Adversarial Networks to Simulate High Energy Physics Detectors / Ríos, John Osorio (Barcelona, Polytechnic U.) ; Armejach, Adrià (Barcelona, Polytechnic U.) ; Khattak, Gulrukh (CERN) ; Petit, Eric (Unlisted, FR) ; Vallecorsa, Sofia (CERN) ; Casas, Marc (Barcelona, Polytechnic U.)
Several hardware companies are proposing native Brain Float 16-bit (BF16) support for neural network training. The usage of Mixed Precision (MP) arithmetic with floating-point 32-bit (FP32) and 16-bit half-precision aims at improving memory and floating-point operations throughput, allowing faster training of bigger models. [...]
2020 - 8 p. - Published in : 10.1109/ICMLA51294.2020.00017
In : 2020 19th IEEE International Conference on Machine Learning and Applications, Online, 14 - 17 Dec 2020, pp.49-56

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