  Convolutional neural networks (CNNs) are a widely used form of deep neural
networks, introducing state-of-the-art results for different problems such as
image classification, computer vision tasks, and speech recognition. However,
CNNs are compute intensive, requiring billions of multiply-accumulate (MAC)
operations per input. To reduce the number of MACs in CNNs, we propose a value
prediction method that exploits the spatial correlation of zero-valued
activations within the CNN output feature maps, thereby saving convolution
operations. Our method reduces the number of MAC operations by 30.4%, averaged
on three modern CNNs for ImageNet, with top-1 accuracy degradation of 1.7%, and
top-5 accuracy degradation of 1.1%.
