PositNN: Training Deep Neural Networks with Mixed Low-Precision Posit
This work addresses efficiency improvements for deep learning practitioners by enabling lower-precision training, though it appears incremental as it builds on prior posit research.
The researchers tackled the problem of training deep convolutional neural networks using low-precision posit formats to reduce memory and hardware costs, finding that 8-bit posits can replace 32-bit floats during training without loss in accuracy or loss.
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.