  A popular track of network compression approach is Quantization aware
Training (QAT), which accelerates the forward pass during the neural network
training and inference. However, not much prior efforts have been made to
quantize and accelerate the backward pass during training, even though that
contributes around half of the training time. This can be partly attributed to
the fact that errors of low-precision gradients during backward cannot be
amortized by the training objective as in the QAT setting. In this work, we
propose to solve this problem by incorporating the gradients into the
computation graph of the next training iteration via a hypernetwork. Various
experiments on CIFAR-10 dataset with different CNN network architectures
demonstrate that our hypernetwork-based approach can effectively reduce the
negative effect of gradient quantization noise and successfully quantizes the
gradients to INT4 with only 0.64 accuracy drop for VGG-16 on CIFAR-10.
