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In this paper, we propose a user-centric design methodology targeting heterogeneous embedded systems-on-chip where communication happens via the network-on-chip approach. More precisely, in this new design methodology, we consider explicitly the information about the user experience and apply machine learning techniques to develop a design flow which aims at minimizing the workload variance; this allows the system for better adapt to different types of user needs and workload variations. Our experimental results show that by considering the user experience into the design space exploration step, the system platforms generated by our approach achieve more than 30% energy savings, on average, compared to the single platform derived from the traditional design flow; this implies that each system configuration we generate is highly suitable for the targeted class of user and workload behaviors.