  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.
