Real-time Super Resolution CNN Accelerator with Constant Kernel Size Winograd Convolution
Yen Po-Wei, Yu‐Sheng Lin, Chia-Yang Chang, Shao‐Yi Chien · 2020
This paper presents a super-resolution CNN de-signed for real-time hardware processing and the associated hardware architecture. Previous networks typically contain numerous layers, various kernel sizes and deconvolution layers, making it hard for hardware implementation. In this paper, we present a CNN only consisting of 3×3 convolution, replacing the deconvolution by pixel shuffling. Such regularity of kernel size enables us to employ Winograd convolution to implement the whole network. The proposed architecture achieves output resolution of 1920×1080 (FHD) at 60 fps while working at a clock frequency of 200 MHz. It also outperforms other 12K-parameter networks in image quality.