  The deployment of Convolutional Neural Networks (CNNs) on resource
constrained platforms such as mobile devices and embedded systems has been
greatly hindered by their high implementation cost, and thus motivated a lot
research interest in compressing and accelerating trained CNN models. Among
various techniques proposed in literature, structured pruning, especially
channel pruning, has gain a lot focus due to 1) its superior performance in
memory, computation, and energy reduction; and 2) it is friendly to existing
hardware and software libraries. In this paper, we investigate the intermediate
results of convolutional layers and present a novel pivoted QR factorization
based channel pruning technique that can prune any specified number of input
channels of any layer. We also explore more pruning opportunities in
ResNet-like architectures by applying two tweaks to our technique. Experiment
results on VGG-16 and ResNet-50 models with ImageNet ILSVRC 2012 dataset are
very impressive with 4.29X and 2.84X computation reduction while only
sacrificing about 1.40\% top-5 accuracy. Compared to many prior works, the
pruned models produced by our technique require up to 47.7\% less computation
while still achieve higher accuracies.
