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Training with larger number of parameters while keeping fast iterations is anincreasingly adopted strategy and trend for developing better performing DeepNeural Network (DNN) models. This necessitates increased memory footprint andcomputational requirements for training. Here we introduce a novel methodologyfor training deep neural networks using 8-bit floating point (FP8) numbers.Reduced bit precision allows for a larger effective memory and increasedcomputational speed. We name this method Shifted and Squeezed FP8 (S2FP8). Weshow that, unlike previous 8-bit precision training methods, the proposedmethod works out-of-the-box for representative models: ResNet-50, Transformerand NCF. The method can maintain model accuracy without requiring fine-tuningloss scaling parameters or keeping certain layers in single precision. Weintroduce two learnable statistics of the DNN tensors - shifted and squeezedfactors that are used to optimally adjust the range of the tensors in 8-bits,thus minimizing the loss in information due to quantization.

Shifted and Squeezed 8-bit Floating Point format for Low-Precision Training of Deep Neural Networks