  A typical deep neural network (DNN) has a large number of trainable
parameters. Choosing a network with proper capacity is challenging and
generally a larger network with excessive capacity is trained. Pruning is an
established approach to reducing the number of parameters in a DNN. In this
paper, we propose a framework for pruning DNNs based on a population-based
global optimization method. This framework can use any pruning objective
function. As a case study, we propose a simple but efficient objective function
based on the concept of energy-based models. Our experiments on ResNets,
AlexNet, and SqueezeNet for the CIFAR-10 and CIFAR-100 datasets show a pruning
rate of more than $50\%$ of the trainable parameters with approximately $<5\%$
and $<1\%$ drop of Top-1 and Top-5 classification accuracy, respectively.

Neural and Evolutionary Computing