  Recent embedded systems are designed with high-performance System-on-Chips
(SoCs) to satisfy the computational needs of complex applications widely used
in real life, such as airplane controllers, autonomous driving automobiles,
medical devices, drones, and hand-held devices. Modern SoCs integrate
multi-core CPUs and various types of accelerators including GPUs and DSPs.
Uncontrolled heat dissipation is one of the main sources of interference that
can adversely affect the reliability and real-time performance of
safety-critical applications. The mechanisms currently available to protect
SoCs from overheating, such as frequency throttling or core shutdown, may
exacerbate the problem as they cause unpredictable delay and deadline misses.
Dynamic changes in ambient temperature further increase the difficulty of
solving this problem.
  This dissertation addresses the challenges caused by thermal interference in
real-time mixed-criticality systems built with heterogeneous embedded SoC
platforms. We propose a novel thermal-aware system framework with analytical
timing and thermal models to guarantee safe execution of real-time tasks under
the thermal constraints of a multi-core CPU/GPU integrated SoC. For
mixed-criticality tasks, the proposed framework bounds the heat generation of
the system at each criticality level and provides different levels of assurance
against ambient temperature changes. In addition, we propose a data-driven
thermal parameter estimation scheme that is directly applicable to MCSs built
with commercial-off-the-shelf multi-core processors to obtain a precise thermal
model without using special measurement instruments or access to proprietary
information. The practicality and effectiveness of our solutions have been
evaluated using real SoC platforms and our contributions will help develop
systems with thermal safety and real-time predictability.
