Novel Learning-Based Task Schedulers for Domain-Specific SoCs

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. IL is one area of machine learning (ML) and a useful method to train artificial intelligence (AI) models by imitating the decisions of an expert or Oracle that knows the optimal solution. This thesis's primary focus is to adapt an ML model to work on-chip and optimize the resource allocation for a set of domain-specific wireless and radar systems applications. Evaluation results with four streaming applications from wireless communications and radar domains show how the proposed IL-based scheduler approximates an offline Oracle expert with more than 97% accuracy and 1.20× faster execution time. The models have been implemented as an add-on, making it easy to port to other SoCs.