ADAPTIVE ONLINE PERFORMANCE AND POWER ESTIMATION FRAMEWORK FOR DYNAMIC RECONFIGURABLE EMBEDDED SYSTEMS

Persistent Link:
http://hdl.handle.net/10150/202745
Title:
ADAPTIVE ONLINE PERFORMANCE AND POWER ESTIMATION FRAMEWORK FOR DYNAMIC RECONFIGURABLE EMBEDDED SYSTEMS
Author:
Mu, Jingqing
Issue Date:
2011
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Runtime dynamic reconfiguration of field-programmable gate arrays (FPGAs) and devices incorporating microprocessors and FPGA has been successfully utilized to increase performance and reduce power consumption. While previous methods have been successful, they typically do not consider the runtime behavior of the application that can be significantly affected by variations in data inputs, user interactions, and environmental conditions. In this dissertation, we present a dynamically reconfigurable system and design methodology that optimizes performance and power consumption by determining which coprocessors to implement with an FPGA based upon the current application behavior.For dynamically reconfigurable systems, in which the selection of hardware coprocessors to implement within the FPGA is determined at runtime, online estimation methods are essential to evaluate the performance and power consumption impact of the hardware coprocessor selection. We present a base profile assisted online system-level performance and power estimation framework for estimating the speedup and power consumption of dynamically reconfigurable embedded systems.Importantly though, complex interactions between multiple application tasks, non-deterministic execution behavior, and effects of operating system scheduling introduce significant challenges. To address these, we further present an adaptive online performance and power estimation framework suing kernel speedup coefficient adaptation that monitors and adapts the changing application and system behavior for multitasked applications. By exhaustively examining predefined voltage and frequency settings for the microprocessor and hardware kernels, the potential speedup and power reduction can be effectively estimated for each configuration and voltage/frequency settings. These estimates can be utilized to determine the optimal system configuration. At the same time, the kernel speedup coefficients for each kernel can be dynamically updated to account for the difference between the estimated and actual performance measured at runtime.Finally, in order to quickly determine kernel selection and voltage and frequency settlings, we present an efficient, online heuristic performance and power estimation framework that significantly decreases execution time at the cost of a small increase in power consumption. This online heuristic estimation framework achieves significant power reduction compared to software only implementation without performance degradation.
Type:
text; Electronic Dissertation
Keywords:
online estimation; performance and power estimation; Electrical & Computer Engineering; adaptable systems; dynamically reconfigurable embedded systems
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Electrical & Computer Engineering
Degree Grantor:
University of Arizona
Advisor:
Lysecky, Roman

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleADAPTIVE ONLINE PERFORMANCE AND POWER ESTIMATION FRAMEWORK FOR DYNAMIC RECONFIGURABLE EMBEDDED SYSTEMSen_US
dc.creatorMu, Jingqingen_US
dc.contributor.authorMu, Jingqingen_US
dc.date.issued2011-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractRuntime dynamic reconfiguration of field-programmable gate arrays (FPGAs) and devices incorporating microprocessors and FPGA has been successfully utilized to increase performance and reduce power consumption. While previous methods have been successful, they typically do not consider the runtime behavior of the application that can be significantly affected by variations in data inputs, user interactions, and environmental conditions. In this dissertation, we present a dynamically reconfigurable system and design methodology that optimizes performance and power consumption by determining which coprocessors to implement with an FPGA based upon the current application behavior.For dynamically reconfigurable systems, in which the selection of hardware coprocessors to implement within the FPGA is determined at runtime, online estimation methods are essential to evaluate the performance and power consumption impact of the hardware coprocessor selection. We present a base profile assisted online system-level performance and power estimation framework for estimating the speedup and power consumption of dynamically reconfigurable embedded systems.Importantly though, complex interactions between multiple application tasks, non-deterministic execution behavior, and effects of operating system scheduling introduce significant challenges. To address these, we further present an adaptive online performance and power estimation framework suing kernel speedup coefficient adaptation that monitors and adapts the changing application and system behavior for multitasked applications. By exhaustively examining predefined voltage and frequency settings for the microprocessor and hardware kernels, the potential speedup and power reduction can be effectively estimated for each configuration and voltage/frequency settings. These estimates can be utilized to determine the optimal system configuration. At the same time, the kernel speedup coefficients for each kernel can be dynamically updated to account for the difference between the estimated and actual performance measured at runtime.Finally, in order to quickly determine kernel selection and voltage and frequency settlings, we present an efficient, online heuristic performance and power estimation framework that significantly decreases execution time at the cost of a small increase in power consumption. This online heuristic estimation framework achieves significant power reduction compared to software only implementation without performance degradation.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectonline estimationen_US
dc.subjectperformance and power estimationen_US
dc.subjectElectrical & Computer Engineeringen_US
dc.subjectadaptable systemsen_US
dc.subjectdynamically reconfigurable embedded systemsen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineElectrical & Computer Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorLysecky, Romanen_US
dc.contributor.committeememberRozenblit, Jerzyen_US
dc.contributor.committeememberLysecky, Susanen_US
dc.contributor.committeememberSzidarovszky, Ferencen_US
dc.contributor.committeememberLysecky, Romanen_US
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