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DTSTART;TZID=America/Chicago:20221116T083000
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UID:submissions.supercomputing.org_SC22_sess274_rpost197@linklings.com
SUMMARY:Accelerated COVID-19 CT Image Enhancement via Sparse Tensor Cores
DESCRIPTION:Posters, Research Posters\n\nAccelerated COVID-19 CT Image Enh
 ancement via Sparse Tensor Cores\n\nChaturvedi, Feng\n\nIn this work we ac
 celerate a target a deep learning model designed to enhance CT images of c
 ovid-19 chest scans namely DD-Net using sparse techniques. The model follo
 ws an auto encoder decoder architecture in deep learning paradigm and has 
 high dimensionality and thus takes many compute hours of training. We prop
 ose a set of techniques which target these two aspects of model - dimensio
 nality and training time. We will implement techniques to prune neurons ma
 king the model sparse and thus reduce the effective dimensionality with a 
 loss of accuracy not more than 5% with minimal additional overhead of retr
 aining. Then we propose set of techniques tailored with respect to underly
 ing hardware in order to better utilize the existing components of hardwar
 e (such as tensor core) and thus reduce time and associated cost required 
 to train this model.\n\nRegistration Category: Tech Program Reg Pass, Exhi
 bits Reg Pass
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