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Accelerated COVID-19 CT Image Enhancement via Sparse Tensor Cores
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DescriptionIn this work we accelerate a target a deep learning model designed to enhance CT images of covid-19 chest scans namely DD-Net using sparse techniques. The model follows an auto encoder decoder architecture in deep learning paradigm and has high dimensionality and thus takes many compute hours of training. We propose a set of techniques which target these two aspects of model - dimensionality and training time. We will implement techniques to prune neurons making the model sparse and thus reduce the effective dimensionality with a loss of accuracy not more than 5% with minimal additional overhead of retraining. Then we propose set of techniques tailored with respect to underlying hardware in order to better utilize the existing components of hardware (such as tensor core) and thus reduce time and associated cost required to train this model.