SWIRL ++ : Evaluating Performance Models to Guide Code Transformation in Convolutional Neural Networks-Reference-Cited by-同舟云学术

SWIRL ++ : Evaluating Performance Models to Guide Code Transformation in Convolutional Neural Networks

Published:2021 Issue: Volume: Page:108-126
ISSN:0302-9743
Container-title:Languages and Compilers for Parallel Computing
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Author:

Patabandi Tharindu R.^ORCID,Venkat Anand^ORCID,Barik Rajkishore^ORCID,Hall Mary^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-72789-5_9

Reference51 articles.

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3. Agarwal, A., et al.: An introduction to computational networks and the computational network toolkit. Technical Report MSR-TR-2014-112 (2014). http://research.microsoft.com/apps/pubs/default.aspx?id=226641

4. Ansel, J., et al.: Opentuner: an extensible framework for program autotuning. In: International Conference on Parallel Architectures and Compilation Techniques. Edmonton, Canada (2014). http://groups.csail.mit.edu/commit/papers/2014/ansel-pact14-opentuner.pdf

5. Baghdadi, R., et al.: Tiramisu: a code optimization framework for high performance systems. arXiv preprint arXiv:1804.10694 (2018)

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