  Mixed precision training (MPT) is becoming a practical technique to improve
the speed and energy efficiency of training deep neural networks by leveraging
the fast hardware support for IEEE half-precision floating point that is
available in existing GPUs. MPT is typically used in combination with a
technique called loss scaling, that works by scaling up the loss value up
before the start of backpropagation in order to minimize the impact of
numerical underflow on training. Unfortunately, existing methods make this loss
scale value a hyperparameter that needs to be tuned per-model, and a single
scale cannot be adapted to different layers at different training stages. We
introduce a loss scaling-based training method called adaptive loss scaling
that makes MPT easier and more practical to use, by removing the need to tune a
model-specific loss scale hyperparameter. We achieve this by introducing
layer-wise loss scale values which are automatically computed during training
to deal with underflow more effectively than existing methods. We present
experimental results on a variety of networks and tasks that show our approach
can shorten the time to convergence and improve accuracy compared to the
existing state-of-the-art MPT and single-precision floating point
