text: The authors propose WAGE, which discretized weights, activations, gradients, and errors at both training and testing time.  By quantization and shifting, SGD training without momentum, and removing the softmax at output layer as well, the model managed to remove all cumbersome computations from every aspect of the model,  thus eliminating the need for a floating point unit completely.  Moreover, by keeping up to 8-bit accuracy, the model performs even better than previously proposed models.  I am eager to see a hardware realization for this method because of its promising results.  The model makes a unified discretization scheme for 4 different kinds of components,  and the accuracy for each of the kind becomes independently adjustable.  This makes the method quite flexible and has the potential to extend to more complicated networks, such as attention or memory.  One caveat is that there seem to be some conflictions in the results shown in Table 1, especially ImageNet.  Given the number of bits each of the WAGE components asked for, a 28.5% top 5 error rate seems even lower than XNOR.  I suspect it is due to the fact that gradients and errors need higher accuracy for real-valued input,  but if that is the case, accuracies on SVHN and CIFAR-10 should also reflect that.  Or, maybe it is due to hyperparameter setting or insufficient training time? Also, dropout seems not conflicting with the discretization.  If there are no other reasons, it would make sense to preserve the dropout in the network as well. In general, the paper was writ ten in good quality and in detail,  I would recommend a clear accept.