2023
DOI: 10.1007/s10489-023-04740-z
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Recursive least squares method for training and pruning convolutional neural networks

Abstract: Convolutional neural networks (CNNs) have shown good performance in many practical applications. However, their high computational and storage requirements make them difficult to deploy on resource-constrained devices. To address this issue, in this paper, we propose a novel iterative structured pruning algorithm for CNNs based on the recursive least squares (RLS) optimization. Our algorithm combines inverse input autocorrelation matrices with weight matrices to evaluate and prune unimportant input channels or… Show more

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Cited by 2 publications
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References 36 publications
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