Proceedings of the ACM International Conference on Supercomputing 2021
DOI: 10.1145/3447818.3459988
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ClickTrain

Abstract: Convolutional neural networks (CNNs) are becoming increasingly deeper, wider, and non-linear because of the growing demand on prediction accuracy and analysis quality. The wide and deep CNNs, however, require a large amount of computing resources and processing time. Many previous works have studied model pruning to improve inference performance, but little work has been done for effectively reducing training cost. In this paper, we propose Click-Train: an efficient and accurate end-to-end training and pruning… Show more

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Cited by 11 publications
(1 citation statement)
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“…RigL [90], ITOP [91], SET [104], DSR [89], and MEST [86], is provided in Tab. Three main sparsity schemes introduced in the area of network pruning consists of unstructured [105][106][107], structured [3,45,[108][109][110][111][112][113][114][115][116][117][118][119], and fine-grained structured pruning [120][121][122][123][124][125][126][127][128][129].…”
Section: Discussionmentioning
confidence: 99%
“…RigL [90], ITOP [91], SET [104], DSR [89], and MEST [86], is provided in Tab. Three main sparsity schemes introduced in the area of network pruning consists of unstructured [105][106][107], structured [3,45,[108][109][110][111][112][113][114][115][116][117][118][119], and fine-grained structured pruning [120][121][122][123][124][125][126][127][128][129].…”
Section: Discussionmentioning
confidence: 99%