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