Learning execution through neural code fusion
Nettet29. jan. 2024 · ICLR'20 Learning Execution through Neural Code Fusion #50. Closed ganler opened this issue Jan 29, 2024 · 4 comments Closed ICLR'20 Learning … Nettet30. aug. 2024 · Deep Neural Networks (DNNs) have emerged as the core enabler of many major applications on mobile devices. To achieve high accuracy, DNN models have become increasingly deep with hundreds or even thousands of operator layers, leading to high memory and computational requirements for inference. Operator fusion (or …
Learning execution through neural code fusion
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Nettet9. jun. 2024 · Learning Execution through Neural Code Fusion As the ... -GNN Encoder, which fuses the messages from the constructed semantic-based graph and attention-based graph to learn comprehensive code semantics. 4) Decoder, which utilizes an attention-based BiLSTM to generate a summary.
NettetAs 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 … NettetThis repository hosts the code for our ICCV2024 paper "Attribute-aided Face Recognition with a Unified Neural Tensor Fusion Network". If you use this code, please cite. …
NettetLearning Execution through Neural Code Fusion. Click To Get Model/Code. 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) … Nettet25. feb. 2024 · [2] Zhou, Yaqin, et al. "Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks." Advances in …
NettetGraph neural networks (GNNs) have emerged as a powerful tool for learning software engineering tasks including code completion, bug finding, and program repair. They benefit from leveraging program structure like control flow graphs, but they are not well-suited to tasks like program execution that require far more sequential reasoning steps …
Nettet17. jun. 2024 · Learning Execution through Neural Code Fusion. As the performance of computer systems stagnates due to the end of Moore's Law, there is a need for new … track it knowhowNettet17. jun. 2024 · In this work, we propose a new approach to use GNNs to learn fused representations of general source code and its execution. Our approach defines a … the rocksinoNettet17. jun. 2024 · This work proposes a new approach to use GNNs to learn fused representations of general source code and its execution that leads to improved … trackit liteNettet26. apr. 2024 · Abstract: 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 static representations of source code, these … trackit itsmNettet25. sep. 2024 · In this work, we propose a new approach using GNNs to learn fused representations of general source code and its execution. Our approach defines a … trackit manager homeNettetIn this work, we propose a new approach to use GNNs to learn fused representations of general source code and its execution. Our approach defines a multi-task GNN over … trackitliveNettet30. aug. 2024 · Deep Neural Networks (DNNs) have emerged as the core enabler of many major applications on mobile devices. To achieve high accuracy, DNN models have … the rock singing its about drive