Rethinking floating point for deep learning

1 Nov 2018Jeff Johnson

Reducing hardware overhead of neural networks for faster or lower power inference and training is an active area of research. Uniform quantization using integer multiply-add has been thoroughly investigated, which requires learning many quantization parameters, fine-tuning training or other prerequisites... (read more)

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