  Weight pruning and weight quantization are two important categories of DNN
model compression. Prior work on these techniques are mainly based on
heuristics. A recent work developed a systematic frame-work of DNN weight
pruning using the advanced optimization technique ADMM (Alternating Direction
Methods of Multipliers), achieving one of state-of-art in weight pruning
results. In this work, we first extend such one-shot ADMM-based framework to
guarantee solution feasibility and provide fast convergence rate, and
generalize to weight quantization as well. We have further developed a
multi-step, progressive DNN weight pruning and quantization framework, with
dual benefits of (i) achieving further weight pruning/quantization thanks to
the special property of ADMM regularization, and (ii) reducing the search space
within each step. Extensive experimental results demonstrate the superior
performance compared with prior work. Some highlights: (i) we achieve 246x,36x,
and 8x weight pruning on LeNet-5, AlexNet, and ResNet-50 models, respectively,
with (almost) zero accuracy loss; (ii) even a significant 61x weight pruning in
AlexNet (ImageNet) results in only minor degradation in actual accuracy
compared with prior work; (iii) we are among the first to derive notable weight
pruning results for ResNet and MobileNet models; (iv) we derive the first
lossless, fully binarized (for all layers) LeNet-5 for MNIST and VGG-16 for
CIFAR-10; and (v) we derive the first fully binarized (for all layers) ResNet
for ImageNet with reasonable accuracy loss.
