Dissertation
Generalized Strictly Periodic Scheduling Analysis, Resource Optimization, and Implementation of Adaptive Streaming Applications
This thesis focuses on addressing four research problems in designing embedded streaming systems.
- Author
- Niknam, S.
- Date
- 25 August 2020
- Links
- Thesis in Leiden Repository
This thesis focuses on addressing four research problems in designing embedded streaming systems. Embedded streaming systems are those systems thatprocess a stream of input data coming from the environment and generate a stream of output data going into the environment. For many embeddedstreaming systems, the timing is a critical design requirement, in which the correct behavior depends on both the correctness of output data and on the time at which the data is produced. An embedded streaming system subjected to such a timing requirement is called a real-time system. Some examples of real-time embedded streaming systems can be found in various autonomous mobile systems, such as planes, self-driving cars, and drones. To handle the tight timing requirements of such real-time embedded streaming systems, modern embedded systems have been equipped with hardware platforms, the so-called Multi-Processor Systems-on-Chip (MPSoC), that contain multiple processors, memories, interconnections, and other hardware peripherals on a single chip, to benefit from parallel execution. To efficiently exploit the computational capacity of an MPSoC platform, a streaming application which is going to be executed on the MPSoC platform must be expressed primarily in a parallel fashion, i.e., the application is represented as a set of parallel executing and communicating tasks. Then, the main challenge is how to schedule the tasks spatially, i.e., task mapping, and temporally, i.e., task scheduling, on the MPSoC platform such that all timing requirements are satisfied while making efficient utilization of available resources (e.g, processors, memory, energy, etc.) on the platform. Another challenge is how to implement and run the mapped and scheduled application tasks on the MPSoC platform. This thesis proposes several techniques to address the aforementioned two challenges.