Modeling and Optimization Frameworks for Runtime Adaptable Embedded Systems

Persistent Link:
http://hdl.handle.net/10150/620835
Title:
Modeling and Optimization Frameworks for Runtime Adaptable Embedded Systems
Author:
Lizarraga, Adrian
Issue Date:
2016
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:
The widespread adoption of embedded computing systems has resulted in the realization of numerous sensing, decision, and control applications with diverse application-specific requirements. However, such embedded systems applications are becoming increasingly difficult to design, simulate, and optimize due to the multitude of interdependent parameters that must be considered to achieve optimal, or near-optimal, performance that meets design constraints. This situation is further exacerbated for data-adaptable embedded systems (DAES) applications due to the dynamic characteristics of the deployment environment and the data streams on which these systems operate. As operating conditions change, these embedded systems must continue to adapt their configuration and composition at runtime in order to meet application requirements. To assist both platform developers and application domain experts, this dissertation presents design and optimization frameworks for the synthesis of runtime adaptable embedded systems. For sensor network applications, we present an initial dynamic profiling and optimization platform that profiles network and sensor node activity to generate optimal node configurations at runtime based on designed-specified application requirements. To support a broader class of DAES applications, we present a modeling and optimization framework that supports the specification of application task flows, data types, and runtime estimation models for the runtime adaptation of task implementations and device mappings. Experimental results for these design and optimization frameworks demonstrate the benefits of dynamic optimization compared to static optimization alternatives. For the presented sensor network and video-based collision avoidance applications, dynamic configurations exhibited improvements of up to 109% and 76%, respectively. Moreover, the performance of the heuristic design space exploration (DSE) algorithms utilized by the runtime optimization frameworks is compared to exhaustive DSE implementations, resulting in speedups of up to 1662X and 544X for the same two applications, respectively.
Type:
text; Electronic Dissertation
Keywords:
Fuzzy Logic; Modeling; Runtime Optimization; Sensor Networks; Electrical & Computer Engineering; 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.isoen_USen
dc.titleModeling and Optimization Frameworks for Runtime Adaptable Embedded Systemsen_US
dc.creatorLizarraga, Adrianen
dc.contributor.authorLizarraga, Adrianen
dc.date.issued2016-
dc.publisherThe University of Arizona.en
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
dc.description.abstractThe widespread adoption of embedded computing systems has resulted in the realization of numerous sensing, decision, and control applications with diverse application-specific requirements. However, such embedded systems applications are becoming increasingly difficult to design, simulate, and optimize due to the multitude of interdependent parameters that must be considered to achieve optimal, or near-optimal, performance that meets design constraints. This situation is further exacerbated for data-adaptable embedded systems (DAES) applications due to the dynamic characteristics of the deployment environment and the data streams on which these systems operate. As operating conditions change, these embedded systems must continue to adapt their configuration and composition at runtime in order to meet application requirements. To assist both platform developers and application domain experts, this dissertation presents design and optimization frameworks for the synthesis of runtime adaptable embedded systems. For sensor network applications, we present an initial dynamic profiling and optimization platform that profiles network and sensor node activity to generate optimal node configurations at runtime based on designed-specified application requirements. To support a broader class of DAES applications, we present a modeling and optimization framework that supports the specification of application task flows, data types, and runtime estimation models for the runtime adaptation of task implementations and device mappings. Experimental results for these design and optimization frameworks demonstrate the benefits of dynamic optimization compared to static optimization alternatives. For the presented sensor network and video-based collision avoidance applications, dynamic configurations exhibited improvements of up to 109% and 76%, respectively. Moreover, the performance of the heuristic design space exploration (DSE) algorithms utilized by the runtime optimization frameworks is compared to exhaustive DSE implementations, resulting in speedups of up to 1662X and 544X for the same two applications, respectively.en
dc.typetexten
dc.typeElectronic Dissertationen
dc.subjectFuzzy Logicen
dc.subjectModelingen
dc.subjectRuntime Optimizationen
dc.subjectSensor Networksen
dc.subjectElectrical & Computer Engineeringen
dc.subjectEmbedded Systemsen
thesis.degree.namePh.D.en
thesis.degree.leveldoctoralen
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineElectrical & Computer Engineeringen
thesis.degree.grantorUniversity of Arizonaen
dc.contributor.advisorLysecky, Romanen
dc.contributor.committeememberLysecky, Romanen
dc.contributor.committeememberAkoglu, Alien
dc.contributor.committeememberSprinkle, Jonathanen
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