USR-LUT: A High-Efficient Universal Super Resolution Accelerator with Lookup Table

Super-resolution (SR) can promote medical diagnosis efficiency by enriching the details of captured images, such as gastroscopy and colonoscopy. However, the wireless capsule detector used for diagnosis is constrained by the camera’s low resolution and limited computing resources, making it difficult to deploy computation- and memory access-intensive SR models. In this paper, we propose an efficient universal SR accelerator based on lookup tables, namely USR-LUT, which can support various SR algorithms. We design a LUT-based computing unit (LCU) with higher efficiency and lower area overhead. By utilizing the sparsity of deconvolution, we propose an efficient data mapping scheme that can flexibly support convolution and deconvolution with different kernel sizes, achieving a 3.24× acceleration for deconvolution. Tile-based computing is adopted to reduce memory resources and external memory access (EMA) overhead. Through experimental evaluation, compared with LUT-based SR algorithms, the USR-LUT achieves the least LUT storage entries of 82k and the smallest LUT resource overhead of 0.078MB, respectively. The USR-LUT achieves the highest area efficiency of 175.7GOPS/mm
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and throughput area ratio (TAR) of 39.9fps/mm
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under the 8-bit precision compared with the state-of-the-art works. To the best of our knowledge, this is the first work of SR accelerators adopting LUT-based computing, which is suitable for tiny mobile devices.