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SoCodeCNN: Program Source Code for Visual CNN Classification Using Computer Vision Methodology
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DOI：10.1109/ACCESS.2019.2949483.png)
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摘要
En 中文
Automated feature extraction from program source-code such that proper computing resources could be allocated to the program is very difficult given the current state of technology. Therefore, conventional methods call for skilled human intervention in order to achieve the task of feature extraction from programs. This research is the first to propose a novel human-inspired approach to automatically convert program source-codes to visual images. The images could be then utilized for automated classification by visual convolutional neural network (CNN) based algorithm. Experimental results show high prediction accuracy in classifying the types of program in a completely automated manner using this approach.
Keyword:
Classification
intermediate representation
LLVM
MPSoC
resource management
program
source code
computer vision
energy consumption
resource optimization
dynamic power management
machine learning
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
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