# Distributed Low Precision Training Without Mixed Precision

arXiv: https://arxiv.org/abs/1911.07384 · HTML: https://mlsanity.com/paper/1911.07384/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [Reduced Precision Floating-Point Optimization for Deep Neural Network On-Device Learning on MicroControllers](https://mlsanity.com/paper/2305.19167/) (2023)
- [Whole Brain Segmentation with Full Volume Neural Network](https://mlsanity.com/paper/2110.15601/) (2021)

## Abstract (arXiv)

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.