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The development of multicore-based embedded real-time systems is a complex process that encompasses several phases. During the software design and development phases (DDP), and prior to the validation phase, key decisions are taken that cover several aspects of the system under development, from hardware selection and configuration, to the identification and mapping of software functions to the processing nodes. The timing dimension steers a large fraction of those decisions as the correctness of the final system ultimately depends on the implemented functions being able to execute within the allotted time budgets. Early execution time figures already in the DDP are thus needed to prevent flawed design decisions resulting in timing misbehavior being intercepted at the timing analysis step in the advanced development phases, when rolling back to different design decisions is extremely onerous. Multicore timing interference compounds this situation as it has been shown to largely impact execution time of tasks and, therefore, must be factored in when deriving early timing bounds. To effectively prevent misconfigurations while preserving resource efficiency, early timing estimates, typically derived from previous projects or early versions of the software functions, should conservatively and tightly overestimate the timing requirements of the final system configuration including multicore contention. In this work, we show that multi-linear regression (MLR) models and neural network (NN) models can be used to predict the impact of multicore contention on tasks' execution time and hence, derive contention-aware early time budgets, as soon as a release (binary) of the application is available. However, those techniques widely used in the mainstream domain minimize the average/mean case and the predicted impact of contention frequently underestimates the impact that can potentially arise at run time. In order to cover this gap, we propose the use of quantile regression neural networks (QRNN), which are specifically designed to predict the desired high quantile. QRNN reduces the number of underestimations compared to MLR and NN models while containing the overestimation by preserving the high quality prediction. For a set of workloads composed by representative kernels running on a NXP T2080 processor, QRNN reduces the number of underestimations to 8.8% compared to 46.8% and 31.3% for MLR and NN models respectively, while keeping the average over estimation in 1%. QRNN exposes a parameter, the target quantile, that allows controlling the behavior of the predictions so it adapts to user’s needs.