Recently, there have been many performance improvements in super resolution using deep learning methods. However, despite the performance improvement, it remains a challenge to reduce the amount of computation for real products. In this letter, we propose a deep network that employs modified Squeezenet's fire modules. We introduce a way to modify the original fire module for effective separation of spatial- and channel-wise learning, and describe how the modified fire modules can be arranged asymmetrically for reducing the number of parameters of the network. Our experiment results show higher PSNR and competitive processing time to other super resolution networks, but with less number of parameters.