  Deep Neural Networks (DNNs) are usually over-parameterized, causing excessive
memory and interconnection cost on the hardware platform. Existing pruning
approaches remove secondary parameters at the end of training to reduce the
model size; but without exploiting the intrinsic network property, they still
require the full interconnection to prepare the network. Inspired by the
observation that brain networks follow the Small-World model, we propose a
novel structural pruning scheme, which includes (1) hierarchically trimming the
network into a Small-World model before training, (2) training the network for
a given dataset, and (3) optimizing the network for accuracy. The new scheme
effectively reduces both the model size and the interconnection needed before
training, achieving a locally clustered and globally sparse model. We
demonstrate our approach on LeNet-5 for MNIST and VGG-16 for CIFAR-10,
decreasing the number of parameters to 2.3% and 9.02% of the baseline model,
respectively.
