Embedded Processor Selection/Performance Estimation using FPGA-based Profiling
In embedded systems, modeling the performance of the candidate processor architectures is very important to enable the designer to estimate the capability of each architecture against the target application. Considering the large number of available embedded processors, the need has increased for bu...
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ndltd-vcu.edu-oai-scholarscompass.vcu.edu-etd-32312017-03-17T08:25:50Z Embedded Processor Selection/Performance Estimation using FPGA-based Profiling Obeidat, Fadi In embedded systems, modeling the performance of the candidate processor architectures is very important to enable the designer to estimate the capability of each architecture against the target application. Considering the large number of available embedded processors, the need has increased for building an infrastructure by which it is possible to estimate the performance of a given application on a given processor with a minimum of time and resources. This dissertation presents a framework that employs the softcore MicroBlaze processor as a reference architecture where FPGA-based profiling is implemented to extract the functional statistics that characterize the target application. Linear regression analysis is implemented for mapping the functional statistics of the target application to the performance of the candidate processor architecture. Hence, this approach does not require running the target application on each candidate processor; instead, it is run only on the reference processor which allows testing many processor architectures in very short time. 2010-07-26T07:00:00Z text application/pdf http://scholarscompass.vcu.edu/etd/2232 http://scholarscompass.vcu.edu/cgi/viewcontent.cgi?article=3231&context=etd © The Author Theses and Dissertations VCU Scholars Compass Embedded Sysytems Performance Modeling FPGA-based Profiling Processor Selection Engineering |
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Embedded Sysytems Performance Modeling FPGA-based Profiling Processor Selection Engineering |
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Embedded Sysytems Performance Modeling FPGA-based Profiling Processor Selection Engineering Obeidat, Fadi Embedded Processor Selection/Performance Estimation using FPGA-based Profiling |
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In embedded systems, modeling the performance of the candidate processor architectures is very important to enable the designer to estimate the capability of each architecture against the target application. Considering the large number of available embedded processors, the need has increased for building an infrastructure by which it is possible to estimate the performance of a given application on a given processor with a minimum of time and resources. This dissertation presents a framework that employs the softcore MicroBlaze processor as a reference architecture where FPGA-based profiling is implemented to extract the functional statistics that characterize the target application. Linear regression analysis is implemented for mapping the functional statistics of the target application to the performance of the candidate processor architecture. Hence, this approach does not require running the target application on each candidate processor; instead, it is run only on the reference processor which allows testing many processor architectures in very short time. |
author |
Obeidat, Fadi |
author_facet |
Obeidat, Fadi |
author_sort |
Obeidat, Fadi |
title |
Embedded Processor Selection/Performance Estimation using FPGA-based Profiling |
title_short |
Embedded Processor Selection/Performance Estimation using FPGA-based Profiling |
title_full |
Embedded Processor Selection/Performance Estimation using FPGA-based Profiling |
title_fullStr |
Embedded Processor Selection/Performance Estimation using FPGA-based Profiling |
title_full_unstemmed |
Embedded Processor Selection/Performance Estimation using FPGA-based Profiling |
title_sort |
embedded processor selection/performance estimation using fpga-based profiling |
publisher |
VCU Scholars Compass |
publishDate |
2010 |
url |
http://scholarscompass.vcu.edu/etd/2232 http://scholarscompass.vcu.edu/cgi/viewcontent.cgi?article=3231&context=etd |
work_keys_str_mv |
AT obeidatfadi embeddedprocessorselectionperformanceestimationusingfpgabasedprofiling |
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1718427723707187200 |