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Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities

Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi” (DEI), Università di Bologna, Via Zamboni 33, 40126 Bologna, Italy
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Academic Editors: Andreas Peter Burg and Minsu Choi
J. Low Power Electron. Appl. 2022, 12(3), 37; https://doi.org/10.3390/jlpea12030037
Received: 4 September 2021 / Revised: 26 April 2022 / Accepted: 26 May 2022 / Published: 5 July 2022
To cope with the increasing complexity of digital systems programming, deep learning techniques have recently been proposed to enhance software deployment by analysing source code for different purposes, ranging from performance and energy improvement to debugging and security assessment. As embedded platforms for cyber-physical systems are characterised by increasing heterogeneity and parallelism, one of the most challenging and specific problems is efficiently allocating computational kernels to available hardware resources. In this field, deep learning applied to source code can be a key enabler to face this complexity. However, due to the rapid development of such techniques, it is not easy to understand which of those are suitable and most promising for this class of systems. For this purpose, we discuss recent developments in deep learning for source code analysis, and focus on techniques for kernel mapping on heterogeneous platforms, highlighting recent results, challenges and opportunities for their applications to cyber-physical systems. View Full-Text
Keywords: cyber-physical systems; heterogeneous device mapping; source code analysis; system optimisation; literature review cyber-physical systems; heterogeneous device mapping; source code analysis; system optimisation; literature review
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MDPI and ACS 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. 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

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