  Convolutional Neural Networks (CNNs) has been applied in numerous Internet of
Things (IoT) devices for multifarious downstream tasks. However, with the
increasing amount of data on edge devices, CNNs can hardly complete some tasks
in time with limited computing and storage resources. Recently, filter pruning
has been regarded as an effective technique to compress and accelerate CNNs,
but existing methods rarely prune CNNs from the perspective of compressing
high-dimensional tensors. In this paper, we propose a novel theory to find
redundant information in three-dimensional tensors, namely Quantified
Similarity between Feature Maps (QSFM), and utilize this theory to guide the
filter pruning procedure. We perform QSFM on datasets (CIFAR-10, CIFAR-100 and
ILSVRC-12) and edge devices, demonstrate that the proposed method can find the
redundant information in the neural networks effectively with comparable
compression and tolerable drop of accuracy. Without any fine-tuning operation,
QSFM can compress ResNet-56 on CIFAR-10 significantly (48.7% FLOPs and 57.9%
parameters are reduced) with only a loss of 0.54% in the top-1 accuracy. For
the practical application of edge devices, QSFM can accelerate MobileNet-V2
inference speed by 1.53 times with only a loss of 1.23% in the ILSVRC-12 top-1
accuracy.
