Source Code Classification for Energy Efficiency in Parallel Ultra Low-Power Microcontrollers

@article{Parisi2021SourceCC,
  title={Source Code Classification for Energy Efficiency in Parallel Ultra Low-Power Microcontrollers},
  author={Emanuele Parisi and Francesco Barchi and Andrea Bartolini and Giuseppe Tagliavini and Andrea Acquaviva},
  journal={2021 Design, Automation \& Test in Europe Conference \& Exhibition (DATE)},
  year={2021},
  pages={878-883}
}
The analysis of source code through machine learning techniques is an increasingly explored research topic aiming at increasing smartness in the software toolchain to exploit modern architectures in the best possible way. In the case of low-power, parallel embedded architectures, this means finding the configuration, for instance in terms of the number of cores, leading to minimum energy consumption. Depending on the kernel to be executed, the energy optimal scaling configuration is not trivial… 

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