Compilation Optimization Pass Selection Using Gate Graph Attention Neural Network for Reliability Improvement

When dealing with different programs or applications, it is necessary to select the appropriate compilation optimization pass or subsequence for the program. Machine learning is widely used as an efficient technological means of solving this problem. However, the most important problem when using ma...

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Veröffentlicht in:	IEEE access 2020-01, Vol.8, p.1-1
Hauptverfasser:	Wu, Jiang, Xu, Jianjun, Meng, Xiankai, Zhang, Haoyu, Zhang, Zhuo, Li, Long
Format:	Artikel
Sprache:	eng
Schlagworte:	Applications programs Compilation optimization Computer Science Computer Science, Information Systems Context Engineering Engineering, Electrical & Electronic extraction of program features Feature extraction GGNN-based model Machine learning Machine learning algorithms Measurement methods Model accuracy Network reliability Neural networks Optimization pass selection program reliability Reliability Science & Technology Semantics Source code Syntactics Technology Telecommunications
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