Learning Execution through Neural Code Fusion

ICLR 2020 Zhan ShiKevin SwerskyDaniel TarlowParthasarathy RanganathanMilad Hashemi

As the performance of computer systems stagnates due to the end of Moore's Law, there is a need for new models that can understand and optimize the execution of general purpose code. While there is a growing body of work on using Graph Neural Networks (GNNs) to learn representations of source code, these representations do not understand how code dynamically executes... (read more)

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