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

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 embedde...

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Bibliographic Details
Published in:Journal of low power electronics and applications 2022-09, Vol.12 (3), p.37
Main Authors: Barchi, Francesco, Parisi, Emanuele, Bartolini, Andrea, Acquaviva, Andrea
Format: Article
Language:English
Subjects:
Algorithms
Complexity
Cyber-physical systems
Deep learning
Digital systems
Heterogeneity
heterogeneous device mapping
Kernels
Keywords
literature review
Machine learning
Optimization
Platforms
Software
Source code
source code analysis
system optimisation
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Summary: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.
ISSN:2079-9268
2079-9268
DOI:10.3390/jlpea12030037