  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.
