  Deep neural networks are typically too computationally expensive to run in
real-time on consumer-grade hardware and low-powered devices. In this paper, we
investigate reducing the computational and memory requirements of neural
networks through network pruning and quantisation. We examine their efficacy on
large networks like AlexNet compared to recent compact architectures:
ShuffleNet and MobileNet. Our results show that pruning and quantisation
compresses these networks to less than half their original size and improves
their efficiency, particularly on MobileNet with a 7x speedup. We also
demonstrate that pruning, in addition to reducing the number of parameters in a
network, can aid in the correction of overfitting.
