A comprehensive study of deep learning compiler bugs-Reference-Cited by-同舟云学术

A comprehensive study of deep learning compiler bugs

Published:2021-08-18 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
language:
Short-container-title:

Author:

Shen Qingchao¹^ORCID,Ma Haoyang¹^ORCID,Chen Junjie¹^ORCID,Tian Yongqiang²^ORCID,Cheung Shing-Chi³^ORCID,Chen Xiang⁴^ORCID

Affiliation:

1. Tianjin University, China

2. University of Waterloo, Canada

3. Hong Kong University of Science and Technology, China

4. Nantong University, China

Funder

National Natural Science Foundation of China

Hong Kong RGC/RIF

Hong Kong ITF

MSRA Collaborative Research Grant

National Key Research and Development Program of China

Hong Kong PhD Fellowship Scheme

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3468264.3468591

Reference78 articles.

1. February 2021. Glow. https://ai.facebook.com/tools/glow/ February 2021. Glow. https://ai.facebook.com/tools/glow/

2. February 2021. Keras. https://keras.io/ February 2021. Keras. https://keras.io/

3. February 2021. nGraph. https://www.intel.com/content/www/us/en/artificial-intelligence/ngraph.html February 2021. nGraph. https://www.intel.com/content/www/us/en/artificial-intelligence/ngraph.html

4. February 2021. PyTorch. https://pytorch.org/ February 2021. PyTorch. https://pytorch.org/

5. February 2021. TensorFlow. https://www.tensorflow.org/ February 2021. TensorFlow. https://www.tensorflow.org/

Cited by 64 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Characterizing and Detecting Program Representation Faults of Static Analysis Frameworks;Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis;2024-09-11

2. Large Language Models Can Connect the Dots: Exploring Model Optimization Bugs with Domain Knowledge-Aware Prompts;Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis;2024-09-11

3. Interoperability in Deep Learning: A User Survey and Failure Analysis of ONNX Model Converters;Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis;2024-09-11

4. Towards Understanding the Bugs in Solidity Compiler;Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis;2024-09-11

5. Fuzzing MLIR Compiler Infrastructure via Operation Dependency Analysis;Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis;2024-09-11