Date of Graduation
5-2015
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Engineering (PhD)
Degree Level
Graduate
Department
Computer Science & Computer Engineering
Advisor/Mentor
Bobda, Christophe
Committee Member
Andrews, David L.
Second Committee Member
Parkerson, James P.
Third Committee Member
Smith, Scott C.
Keywords
Embedded systems; Field programmable gate arrays; Image processing; Optimization; Systems-on-chip; Verification
Abstract
In this dissertation, a method and a tool to enable design and verification of computation demanding embedded vision-based systems is presented. Starting with an executable specification in OpenCV, we provide subsequent refinements and verification down to a system-on-chip prototype into an FPGA-Based smart camera. At each level of abstraction, properties of image processing applications are used along with structure composition to provide a generic architecture that can be automatically verified and mapped to the lower abstraction level. The result is a framework that encapsulates the computer vision library OpenCV at the highest level, integrates Accelera's System-C/TLM with UVM and QEMU-OS for virtual prototyping and verification and mapping to a lower level, the last of which is the FPGA. This will relieve hardware designers from time-consuming and error-prone manual implementations, thus allowing them to focus on other steps of the design process. We also propose a novel streaming interface, called Component Interconnect and Data Access (CIDA), for embedded video designs, along with a formal model and a component composition mechanism to cluster components in logical and operational groups that reduce resource usage and power consumption.
Citation
Mefenza Nentedem, M. (2015). Design and Verification Environment for High-Performance Video-Based Embedded Systems. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/13
Included in
Computer Engineering Commons, Computer Sciences Commons, VLSI and Circuits, Embedded and Hardware Systems Commons