PositNN: Training Deep Neural Networks with Mixed Low-Precision Posit

TL;DR

This paper explores the use of low-precision posit formats for training deep neural networks, demonstrating that 8-bit posits can replace 32-bit floats without loss of accuracy.

Contribution

It introduces a software framework for training neural networks with mixed low-precision posits, showing their viability as an alternative to traditional floating-point formats.

Findings

8-bit posits can replace 32-bit floats in training without accuracy loss

Mixed precision training with posits is feasible and effective

Posit format reduces memory and hardware resource requirements

Abstract

Low-precision formats have proven to be an efficient way to reduce not only the memory footprint but also the hardware resources and power consumption of deep learning computations. Under this premise, the posit numerical format appears to be a highly viable substitute for the IEEE floating-point, but its application to neural networks training still requires further research. Some preliminary results have shown that 8-bit (and even smaller) posits may be used for inference and 16-bit for training, while maintaining the model accuracy. The presented research aims to evaluate the feasibility to train deep convolutional neural networks using posits. For such purpose, a software framework was developed to use simulated posits and quires in end-to-end training and inference. This implementation allows using any bit size, configuration, and even mixed precision, suitable for different precision requirements in various stages. The obtained results suggest that 8-bit posits can substitute 32-bit floats during training with no negative impact on the resulting loss and accuracy.