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ASU Electronic Theses and Dissertations


This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.


Stream processing has emerged as an important model of computation especially in the context of multimedia and communication sub-systems of embedded System-on-Chip (SoC) architectures. The dataflow nature of streaming applications allows them to be most naturally expressed as a set of kernels iteratively operating on continuous streams of data. The kernels are computationally intensive and are mainly characterized by real-time constraints that demand high throughput and data bandwidth with limited global data reuse. Conventional architectures fail to meet these demands due to their poorly matched execution models and the overheads associated with instruction and data movements. This work presents StreamWorks, …

Contributors
Panda, Amrit Kumar, Chatha, Karam S., Wu, Carole-Jean, et al.
Created Date
2014

The ubiquity of embedded computational systems has exploded in recent years impacting everything from hand-held computers and automotive driver assistance to battlefield command and control and autonomous systems. Typical embedded computing systems are characterized by highly resource constrained operating environments. In particular, limited energy resources constrain performance in embedded systems often reliant on independent fuel or battery supplies. Ultimately, mitigating energy consumption without sacrificing performance in these systems is paramount. In this work power/performance optimization emphasizing prevailing data centric applications including video and signal processing is addressed for energy constrained embedded systems. Frameworks are presented which exchange quality of service …

Contributors
Baker, Michael, Chatha, Karam S., Raupp, Gregory B., et al.
Created Date
2011