  The task of accelerating large neural networks on general purpose hardware
has, in recent years, prompted the use of channel pruning to reduce network
size. However, the efficacy of pruning based approaches has since been called
into question. In this paper, we turn to distillation for model
compression---specifically, attention transfer---and develop a simple method
for discovering performance enhanced student networks. We combine channel
saliency metrics with empirical observations of runtime performance to design
more accurate networks for a given latency budget. We apply our methodology to
residual and densely-connected networks, and show that we are able to find
resource-efficient student networks on different hardware platforms while
maintaining very high accuracy. These performance-enhanced student networks
achieve up to 10% boosts in top-1 ImageNet accuracy over their channel-pruned
counterparts for the same inference time.
