Barchi, F.; Parisi, E.; Bartolini, A.; Acquaviva, A.
Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities. J. Low Power Electron. Appl. 2022, 12, 37.
https://doi.org/10.3390/jlpea12030037
AMA Style
Barchi F, Parisi E, Bartolini A, Acquaviva A.
Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities. Journal of Low Power Electronics and Applications. 2022; 12(3):37.
https://doi.org/10.3390/jlpea12030037
Chicago/Turabian Style
Barchi, Francesco, Emanuele Parisi, Andrea Bartolini, and Andrea Acquaviva.
2022. "Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities" Journal of Low Power Electronics and Applications 12, no. 3: 37.
https://doi.org/10.3390/jlpea12030037
APA Style
Barchi, F., Parisi, E., Bartolini, A., & Acquaviva, A.
(2022). Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities. Journal of Low Power Electronics and Applications, 12(3), 37.
https://doi.org/10.3390/jlpea12030037