We introduce a DNN training technique that learns only a fraction of the full
parameter set without incurring an accuracy penalty. To do this, our algorithm
constrains the total number of weights updated during backpropagation to those
with the highest total gradients. The remaining weights are not tracked, and
their initial value is regenerated at every access to avoid storing them in
memory. This can dramatically reduce the number of off-chip memory accesses
during both training and inference, a key component of the energy needs of DNN
accelerators. By ensuring that the total weight diffusion remains close to that
of baseline unpruned SGD, networks pruned using our technique are able to
retain state-of-the-art accuracy across network architectures -- including
networks previously identified as difficult to compress, such as Densenet and
WRN. With ResNet18 on ImageNet, we observe an 11.7$\times$ weight reduction
with no accuracy loss, and up to 24.4$\times$ with a small accuracy impact.

本研究提出了一种 DNN 训练技术，该技术可以在不影响准确率的情况下，只学习了部分全参数集。此方法使用反向传播约束更新权重的总数，以仅跟踪具有最高总梯度的权重。通过确保总权重扩散保持接近于基线未修剪 SGD 的扩散，使用我们技术进行修剪的网络能够在网络架构中保留领先的状态，包括先前被认为难以压缩的网络。我们在 ImageNet 上使用 ResNet18 观察到了 11.7 倍的权重减少无准确性损失，最多可达 24.4 倍，但具有小的准确性影响。