In embedded computer systems there are often tasks, implemented as stand-alone devices,
that are both application-specific and compute intensive. A recurring problem
in this area is to design these application-specific embedded systems as close to the
power and efficiency envelope as possible. Work has been done on optimizing singlecore
systems and memory organisation, but current methods for achieving system design
goals are proving limited as the system capabilities and system size increase in the
multi- and many-core era. To address this problem, this thesis investigates machine
learning approaches to managing the design space presented in the interconnect design
of embedded multi-core systems. The design space presented is large due to the
system scale and level of interconnectivity, and also feature inter-dependant parameters,
further complicating analysis. The results presented in this thesis demonstrate
that machine learning approaches, particularly wkNN and random forest, work well
in handling the complexity of the design space. The benefits of this approach are in
automation, saving time and effort in the system design phase as well as energy and
execution time in the finished system
