  Deep neural networks have been remarkable successful in various AI tasks but
often cast high computation and energy cost for energy-constrained applications
such as mobile sensing. We address this problem by proposing a novel framework
that optimizes the prediction accuracy and energy cost simultaneously, thus
enabling effective cost-accuracy trade-off at test time. In our framework, each
data instance is pushed into a cascade of deep neural networks with increasing
sizes, and a selection module is used to sequentially determine when a
sufficiently accurate classifier can be used for this data instance. The
cascade of neural networks and the selection module are jointly trained in an
end-to-end fashion by the REINFORCE algorithm to optimize a trade-off between
the computational cost and the predictive accuracy. Our method is able to
simultaneously improve the accuracy and efficiency by learning to assign easy
instances to fast yet sufficiently accurate classifiers to save computation and
energy cost, while assigning harder instances to deeper and more powerful
classifiers to ensure satisfiable accuracy. With extensive experiments on
several image classification datasets using cascaded ResNet classifiers, we
demonstrate that our method outperforms the standard well-trained ResNets in
accuracy but only requires less than 20% and 50% FLOPs cost on the CIFAR-10/100
datasets and 66% on the ImageNet dataset, respectively.
