# Deep Learning Training on the Edge with Low-Precision Posits

arXiv: https://arxiv.org/abs/1907.13216 · HTML: https://mlsanity.com/paper/1907.13216/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2019.

## Built on by (most cited first, 4 of 4)

- [Training Transformers with 4-bit Integers](https://mlsanity.com/paper/2306.11987/) (2023)
- [GSQ-Tuning: Group-Shared Exponents Integer in Fully Quantized Training for LLMs On-Device Fine-tuning](https://mlsanity.com/paper/2502.12913/) (2025)
- [PLAM: a Posit Logarithm-Approximate Multiplier](https://mlsanity.com/paper/2102.09262/) (2021)
- [PositNN: Training Deep Neural Networks with Mixed Low-Precision Posit](https://mlsanity.com/paper/2105.00053/) (2021)

## Abstract (arXiv)

Recently, the posit numerical format has shown promise for DNN data representation and compute with ultra-low precision ([5..8]-bit). However, majority of studies focus only on DNN inference. In this work, we propose DNN training using posits and compare with the floating point training. We evaluate on both MNIST and Fashion MNIST corpuses, where 16-bit posits outperform 16-bit floating point for end-to-end DNN training.