Raza, Syed AounVallavanthara, Amal JoseNidavan, RakeshKelter, Udo2024-07-262024-07-2620200720-8928https://dl.gi.de/handle/20.500.12116/44147Multi-core software should be partitioned under different constraints e.g., balanced execution load on cores, timing behavior and optimized level of communication/ synchronization among different system components. The objective is to efficiently distribute the processes onto multi-core hardware such that the system has reduced communication/ synchronization complexity. Moreover, a bad distribution strategy during migration from single- to multi-core and from multi- to many-core hardware does not always return the expected performance gain. This paper presents two novel AI-based approaches for optimal distribution (minimal inter-core communication inspite of no deadline misses) of software system on multi-core hardware architecture. We discuss the comparisons of our machine learning solutions based on unsupervised and reinforcement learning. We share the benefits and limitations of using unsupervised learning and reinforcement learning based on our experience.enmulti-core softwarepartitioningloadsynchronizationdistribution strategyperformancemachine learningText/Conference Paper