This Master’s thesis includes the design, integration on-chip, and evaluation of a set of imitation learning (IL)-based scheduling policies: deep neural network (DNN)and decision tree (DT). We first developed IL-based scheduling policies for heterogeneous systems-on-chips (SoCs). Then, we tested these policies using a system-level domain-specific system-on-chip simulation framework . Finally, we transformed them into efficient code using a cloud engine  and implemented on a user-space emulation framework  on a Unix-based SoC.
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- Masters Thesis Computer Engineering 2020