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 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.

Automated summary

To cope with the complexity of programming digital systems, deep learning techniques have been proposed for source code analysis, particularly for efficient kernel mapping on heterogeneous platforms. However, it is challenging to determine which techniques are most suitable for cyber-physical systems. This paper discusses recent developments in deep learning for source code analysis and highlights the opportunities and challenges for their application to this class of systems.