Architectural level hierarchical power estimation of control units

Chen, R.Y.; Irwin, M.J.; Bajwa, R.S.

doi:10.1109/asic.1998.722905

Cited by 3 publications

(6 citation statements)

References 10 publications

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“…The technique proposed in the work of Chen et al has considered the format of instructions to estimate energy consumption at processor's control unit. Moreover, it considered transition signal among coresident instructions to investigate inter‐instruction power consumption effects.…”

Section: Code Analysis–based Energy Estimation and Modelingmentioning

confidence: 99%

“…A similar to the work of Chen et al but detailed simulation‐based approach has been discussed in the work of Chang et al to investigate per cycle energy consumption for ARM7 processor. The proposed simulator estimates architectural level energy based on the charge transfer within processor's circuit, and also, it is robust to noise.…”

Section: Code Analysis–based Energy Estimation and Modelingmentioning

confidence: 99%

“…Instruction power model is based on CPU cycle to fetch, decode, and execute a target instruction in addition to power consumption during cache hit/miss. Existing state‐of‐the‐art estimation schemes, such as in the works of several authors, considered architectural details attribute to construct per‐instruction power models. On the other hand, other works constructed instruction power models based on profiling through extremal power measuring hardware device.…”

Section: Code Analysis–based Energy Estimation and Modelingmentioning

confidence: 99%

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A survey on energy estimation and power modeling schemes for smartphone applications

Ahmad

Gani

Hamid

et al. 2016

Int J Communication

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In the last decade, the rising trend in the popularity of smartphones motivated software developers to increase application functionality. However, increasing application functionality demands extra power budget that as a result, decreases smartphone battery lifetime. Optimizing energy critical sections of an application creates an opportunity to increase battery lifetime. Smartphone application energy estimation helps investigate energy consumption behavior of an application at diversified granularity (eg, coarse and fine granular) for optimal battery resource use. This study explores energy estimation and modeling schemes to highlight their advantages and shortcomings. It classifies existing smartphone application energy estimation and modeling schemes into 2 categories, ie, code analysis and mobile components power model-based estimation owing to their architectural designs. Moreover, it further classifies code analysis-based modeling and estimation schemes in simulation-based and profiling-based categories. It compares existing energy estimation and modeling schemes based on a set of parameters common in most literature to highlight the commonalities and differences among reported literature. Existing application energy estimation schemes are low-accurate, resource expensive, or non-scalable, as they consider marginally accurate smart battery's voltage/ current sensors, low-rate power capturing tools, and labor-driven lab-setting environment to propose power models for smartphone application energy estimation. Besides, the energy estimation overhead of the components power model-based estimation schemes is very high as they physically run the application on a smartphone for energy profiling. To optimize smartphone application energy estimation, we have highlighted several research issues to help researchers of this domain to understand the problem clearly. KEYWORDS application energy, energy estimation, energy profiling, profiling overhead 1 | INTRODUCTION Nowadays, with the proliferation of portable devices, the energy-efficient system design has become a must-to-meet requirement for recent resource-constrained smartphone devices. The ever growing smartphone user's demands encourage application developers to enrich the legacy applications that as a result, increase smartphone's computational and communication cost. Among all smartphone-based applications, on-demand video, 1 multi-agent-based distributed games, 2 spatial locality-based social applications, 3 pedestrian tracking, 4,5 and context-based advertisement services 6 are the most energy-hungry services. The inherent features of these services considerably increase the energy demands of processors when executed on smartphones. 7-9 It is estimated that in last 2 decades, processor power budget has increased significantly because of smartphone application's high resource use. To efficiently exploit smartphone's power budget, software optimization minimizes inter-component interaction among hardware modules that as a result, increases battery's lifetime. ...

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Section: Code Analysis–based Energy Estimation and Modelingmentioning

confidence: 99%

Section: Code Analysis–based Energy Estimation and Modelingmentioning

confidence: 99%

Section: Code Analysis–based Energy Estimation and Modelingmentioning

confidence: 99%

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A survey on energy estimation and power modeling schemes for smartphone applications

Ahmad

Gani

Hamid

et al. 2016

Int J Communication

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“…For the functional units in the datapath, a set of reusable energy models have been developed. A novel efficient hierarchical method [Chen et al 1998b] to characterize the control unit's power consumption was adopted into the estimator. It is based on the instruction format transitions and output signal transitions instead of pure input or state transitions.…”

Section: Introductionmentioning

confidence: 99%

Architecture-level power estimation and design experiments

Chen

Irwin

Bajwa

2001

ACM Trans. Des. Autom. Electron. Syst.

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Architecture-level power estimation has received more attention recently because of its efficiency. This article presents a technique used to do power analysis of processors at the architecture level. It provides cycle-by-cycle power consumption data of the architecture on the basis of the instruction/data flow stream. To characterize the power dissipation of control units, a novel hierarchical method has been developed. Using this technique, a power estimator is implemented for a commercial processor. The accuracy of the estimator is validated by comparing the power values it produces against measurements made by a gate-level power simulator for the same benchmark set. Our estimation approach is shown to provide very efficient and accurate power analysis at the architecture level. The energy models built for first-pass estimation (such as ALU, MAC unit, register files) are reusable for future architecture design modification. In this article, we demonstrate the application of the technique. Furthermore, this technique can evaluate various kinds of software to achieve hardware/software codesign for low power.

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“…Several researchers have proposed RTL power models for controllers. A few approaches assume the availability of a gate-level implementation of the controller [1,2]. In this case, the power macro-model can be tuned directly on the implementation.…”

Section: Introductionmentioning

confidence: 99%

Regression-based RTL power models for controllers

Benini

Bogliolo

Macii

et al. 2000

Proceedings of the 10th Great Lakes Symposium on VLSI

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Power consumption of the controller of an RTL design, though typically smaller than that of the datapath, cannot be neglected. Controller power modeling is a more challenging task than that of datapath modules, because the controller is usually specified in an abstract fashion that has no structural relationship with its final implementation. This paper presents an extensive experimental study on pre.state assignment power models. Starting from a large set of controllers, we study the relation between power and static parameters, such as n~.mber of inputs and outputs, as well as dynamic parameters, such as input and output switching activity. We then quentin the loss of accuracy caused by the high level of abstraction of the models, and we show how previously published results, obtained under favorable ex. perimental conditions, have underestimated model inaccuracy. Finally, we perform extensive exploration on a large number of alternative macro-model structures, and we select an optimal model equation.

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Architectural level hierarchical power estimation of control units

Cited by 3 publications

References 10 publications

A survey on energy estimation and power modeling schemes for smartphone applications

A survey on energy estimation and power modeling schemes for smartphone applications

Architecture-level power estimation and design experiments

Regression-based RTL power models for controllers

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