ICLR 2018 Reproducibility Challenge
Training and Inference with Integers in Deep Neural Networks
arxiv (original paper)
We reproduce Wu et al.'s ICLR 2018 submission Training And Inference With Integers In Deep Neural Networks. The proposed `WAGE' model reduces floating-point precision with only a slight reduction in accuracy. The paper introduces two novel approaches which allow for the use of integer values by quantizing weights, activation, gradients and errors in both training and inference. We reproduce the WAGE model, trained on the CIFAR10 dataset. The methodology demonstrated in this paper has applications for use with Application Specific Integrated Circuit (ASICs).