Average-case analysis of power consumption in embedded systems

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dc.contributor.advisor Popovici, Emanuel M. en
dc.contributor.author Zeinolabedini, Nasim
dc.date.accessioned 2016-12-13T14:00:39Z
dc.date.available 2016-12-13T14:00:39Z
dc.date.issued 2015
dc.date.submitted 2015
dc.identifier.citation Zeinolabedini, N. 2015. Average-case analysis of power consumption in embedded systems. PhD Thesis, University College Cork. en
dc.identifier.endpage 126 en
dc.identifier.uri http://hdl.handle.net/10468/3375
dc.description.abstract Power efficiency is one of the most important constraints in the design of embedded systems since such systems are generally driven by batteries with limited energy budget or restricted power supply. In every embedded system, there are one or more processor cores to run the software and interact with the other hardware components of the system. The power consumption of the processor core(s) has an important impact on the total power dissipated in the system. Hence, the processor power optimization is crucial in satisfying the power consumption constraints, and developing low-power embedded systems. A key aspect of research in processor power optimization and management is “power estimation”. Having a fast and accurate method for processor power estimation at design time helps the designer to explore a large space of design possibilities, to make the optimal choices for developing a power efficient processor. Likewise, understanding the processor power dissipation behaviour of a specific software/application is the key for choosing appropriate algorithms in order to write power efficient software. Simulation-based methods for measuring the processor power achieve very high accuracy, but are available only late in the design process, and are often quite slow. Therefore, the need has arisen for faster, higher-level power prediction methods that allow the system designer to explore many alternatives for developing powerefficient hardware and software. The aim of this thesis is to present fast and high-level power models for the prediction of processor power consumption. Power predictability in this work is achieved in two ways: first, using a design method to develop power predictable circuits; second, analysing the power of the functions in the code which repeat during execution, then building the power model based on average number of repetitions. In the first case, a design method called Asynchronous Charge Sharing Logic (ACSL) is used to implement the Arithmetic Logic Unit (ALU) for the 8051 microcontroller. The ACSL circuits are power predictable due to the independency of their power consumption to the input data. Based on this property, a fast prediction method is presented to estimate the power of ALU by analysing the software program, and extracting the number of ALU-related instructions. This method achieves less than 1% error in power estimation and more than 100 times speedup in comparison to conventional simulation-based methods. In the second case, an average-case processor energy model is developed for the Insertion sort algorithm based on the number of comparisons that take place in the execution of the algorithm. The average number of comparisons is calculated using a high level methodology called MOdular Quantitative Analysis (MOQA). The parameters of the energy model are measured for the LEON3 processor core, but the model is general and can be used for any processor. The model has been validated through the power measurement experiments, and offers high accuracy and orders of magnitude speedup over the simulation-based method. en
dc.description.sponsorship Science Foundation Ireland (SFI Grant 07/IN.1/I97) en
dc.format.mimetype application/pdf en
dc.language English en
dc.language.iso en en
dc.publisher University College Cork en
dc.rights © 2015. Nasim Zeinolabedini. en
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/ en
dc.subject Embedded systems en
dc.subject Processor power estimation en
dc.title Average-case analysis of power consumption in embedded systems en
dc.type Doctoral thesis en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PHD (Engineering) en
dc.internal.availability Full text available en
dc.check.info No embargo required en
dc.description.version Accepted Version
dc.contributor.funder Science Foundation Ireland en
dc.description.status Not peer reviewed en
dc.internal.school Electrical and Electronic Engineering en
dc.check.type No Embargo Required
dc.check.reason No embargo required en
dc.check.opt-out Not applicable en
dc.thesis.opt-out false
dc.check.embargoformat Not applicable en
ucc.workflow.supervisor e.popovici@ucc.ie
dc.internal.conferring Autumn 2015 en


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© 2015. Nasim Zeinolabedini. Except where otherwise noted, this item's license is described as © 2015. Nasim Zeinolabedini.
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