  Quantization of deep neural networks is a promising approach that reduces the
inference cost, making it feasible to run deep networks on resource-restricted
devices. Inspired by existing methods, we propose a new framework to learn the
quantization intervals (discrete values) using the knowledge of the network's
weight and activation distributions, i.e., standard deviation. Furthermore, we
propose a novel base-2 logarithmic quantization scheme to quantize weights to
power-of-two discrete values. Our proposed scheme allows us to replace
resource-hungry high-precision multipliers with simple shift-add operations.
According to our evaluations, our method outperforms existing work on CIFAR10
and ImageNet datasets and even achieves better accuracy performance with 3-bit
weights and activations when compared to the full-precision models. Moreover,
our scheme simultaneously prunes the network's parameters and allows us to
flexibly adjust the pruning ratio during the quantization process.
