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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

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 [11]. Finally, we transformed them into efficient code using a cloud engine [1] and implemented on a user-space emulation framework [61] on a Unix-based SoC.

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Date Created
  • 2020
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  • Text
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    • Masters Thesis Computer Engineering 2020

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