  Deep Convolutional Neural Networks (CNNs) are widely employed in modern
computer vision algorithms, where the input image is convolved iteratively by
many kernels to extract the knowledge behind it. However, with the depth of
convolutional layers getting deeper and deeper in recent years, the enormous
computational complexity makes it difficult to be deployed on embedded systems
with limited hardware resources. In this paper, we propose two
computation-performance optimization methods to reduce the redundant
convolution kernels of a CNN with performance and architecture constraints, and
apply it to a network for super resolution (SR). Using PSNR drop compared to
the original network as the performance criterion, our method can get the
optimal PSNR under a certain computation budget constraint. On the other hand,
our method is also capable of minimizing the computation required under a given
PSNR drop.
