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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.


With the end of Dennard scaling and Moore's law, architects have moved towards heterogeneous designs consisting of specialized cores to achieve higher performance and energy efficiency for a target application domain. Applications of linear algebra are ubiquitous in the field of scientific computing, machine learning, statistics, etc. with matrix computations being fundamental to these linear algebra based solutions. Design of multiple dense (or sparse) matrix computation routines on the same platform is quite challenging. Added to the complexity is the fact that dense and sparse matrix computations have large differences in their storage and access patterns and are difficult to …

Contributors
Animesh, Saurabh, Chakrabarti, Chaitali, Brunhaver, John, et al.
Created Date
2018

In recent years, we have observed the prevalence of stream applications in many embedded domains. Stream programs distinguish themselves from traditional sequential programming languages through well defined independent actors, explicit data communication, and stable code/data access patterns. In order to achieve high performance and low power, scratch pad memory (SPM) has been introduced in today's embedded multicore processors. Current design frameworks for developing stream applications on SPM enhanced embedded architectures typically do not include a compiler that can perform automatic partitioning, mapping and scheduling under limited on-chip SPM capacities and memory access delays. Consequently, many designs are implemented manually, which …

Contributors
Che, Weijia, Chatha, Karam Singh, Chatha, Karam Singh, et al.
Created Date
2012