Distributed Low Precision Training Without Mixed Precision
This work addresses the problem of reducing hardware resource usage for deep learning practitioners, but it appears incremental as it builds on existing low precision training strategies.
The paper tackled the problem of training deep models without mixed precision by proposing a solution using only IEEE FP-16 format throughout training, and found that it is not essential to use FP32, achieving results tested on ResNet50 with 128 GPUs on ImageNet-full.
Low precision training is one of the most popular strategies for deploying the deep model on limited hardware resources. Fixed point implementation of DCNs has the potential to alleviate complexities and facilitate potential deployment on embedded hardware. However, most low precision training solution is based on a mixed precision strategy. In this paper, we have presented an ablation study on different low precision training strategy and propose a solution for IEEE FP-16 format throughout the training process. We tested the ResNet50 on 128 GPU cluster on ImageNet-full dataset. We have viewed that it is not essential to use FP32 format to train the deep models. We have viewed that communication cost reduction, model compression, and large-scale distributed training are three coupled problems.