DevScope

Jung, Wonwoo; Kang, Chulkoo; Yoon, Chanmin; Kim, Donwon; Cha, Hojung

doi:10.1145/2380445.2380502

Cited by 79 publications

(5 citation statements)

References 10 publications

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“…However, they require a data gathering and a training time that might come at a high computational cost. Models constructed and trained offline would avoid the drawbacks of the first category [18,57], but they are always device-specific and their accuracy varies from one device to the other.…”

Section: Model Construction and Implementationmentioning

confidence: 99%

Power profiling and monitoring in embedded systems: A comparative study and a novel methodology based on NARX neural networks

Djedidi

Djeziri

2020

Journal of Systems Architecture

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Power consumption in electronic systems is an essential feature for the management of energy autonomy, performance analysis, and the aging monitoring of components. Thus, several research studies have been devoted to the development of power models and profilers for embedded systems. Each of these models is designed to fit a specific usage context. This paper is a part of a series of works dedicated to modeling and monitoring embedded systems in airborne equipment. The objective of this paper is twofold. Firstly, it presents an overview of the most used models in the literature. Then, it offers a comparative analysis of these models according to a set of criteria, such as the modeling assumptions, the necessary instrumentation necessary, the accuracy, and the complexity of implementation.Secondly, we introduce a new power estimator for ARM-Based embedded systems, with component-level granularity. The estimator is based on NARX neural networks and used to monitor power for diagnosis purposes. The obtained experimental results highlight the advantages and limitations of the models presented in the literature and demonstrate the effectiveness of the proposed NARX, having obtained the best results in its class for a smartphone (An online Mean Absolute Percentage Error = 2.2%).

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Section: Model Construction and Implementationmentioning

confidence: 99%

Power profiling and monitoring in embedded systems: A comparative study and a novel methodology based on NARX neural networks

Djedidi

Djeziri

2020

Journal of Systems Architecture

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“…Of these profilers, there are those built by system providers like Google's Android Power Profiler [20,21], and Qualcomm's Trepn Profiler [22]. We also cite works published in the literature such as PowerBooter [23], Sesame [24], and DevScope [25].…”

Section: Power Consumption Modeling In Embedded Socsmentioning

confidence: 99%

“…Most of the models estimating power consumption use either finite state machines (FSM) like Eprof [34] and DevScope [25], or the more popular regression-based models as is the case for at least Kim et al [26], Kim et al [27], Shukla et al [30], and Xu et al [35], or a combination of both Jung et al [25]. There is also a new trend of using nonlinear methods like neural networks [36][37][38].…”

Section: Power Consumption Modeling In Embedded Socsmentioning

confidence: 99%

Incremental Modeling and Monitoring of Embedded CPU-GPU Chips

Djedidi

Djeziri

2020

Processes

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This paper presents a monitoring framework to detect drifts and faults in the behavior of the central processing unit (CPU)-graphics processing unit (GPU) chips powering them. To construct the framework, an incremental model and a fault detection and isolation (FDI) algorithm are hereby proposed. The reference model is composed of a set of interconnected exchangeable subsystems that allows it to be adapted to changes in the structure of the system or operating modes, by replacing or extending its components. It estimates a set of variables characterizing the operating state of the chip from only two global inputs. Then, through analytical redundancy, the estimated variables are compared to the output of the system in the FDI module, which generates alarms in the presence of faults or drifts in the system. Furthermore, the interconnected nature of the model allows for the direct localization and isolation of any detected abnormalities. The implementation of the proposed framework requires no additional instrumentation as the used variables are measured by the system. Finally, we use multiple experimental setups for the validation of our approach and also proving that it can be applied to most of the existing embedded systems.

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“…They are usually employed to record the devices' energy consumption associated with their execution time, and analyze the energy consumption pattern of target programs. The second type of profiling is called modelbased profiling [18][19][20][21][22][23], which provides the pre-defined instructions/functions tables associated with hardware energy consumptions. It can be further classified into high-level model [20,21] and low-level model [18,19,[23][24][25].…”

Section: Previous Workmentioning

confidence: 99%

“…It records the events due to function invocations in a database and analyzes the average energy consumption for individual functions. Jung et al [20] propose an energy prediction method for smartphones called DevScope, which controls the components on-the-fly using battery monitoring unit (BMU) updating rate, and develops the component power model automatically by analyzing the changes of power state. Low-level model is associated with energy consumption patterns constructed by instruction-level or architecture-level.…”

Section: Previous Workmentioning

confidence: 99%

A Machine Learning Method for Power Prediction on the Mobile Devices

Chen

Chen²,

Chen³

et al. 2015

J Med Syst

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Energy profiling and estimation have been popular areas of research in multicore mobile architectures. While short sequences of system calls have been recognized by machine learning as pattern descriptions for anomalous detection, power consumption of running processes with respect to system-call patterns are not well studied. In this paper, we propose a fuzzy neural network (FNN) for training and analyzing process execution behaviour with respect to series of system calls, parameters and their power consumptions. On the basis of the patterns of a series of system calls, we develop a power estimation daemon (PED) to analyze and predict the energy consumption of the running process. In the initial stage, PED categorizes sequences of system calls as functional groups and predicts their energy consumptions by FNN. In the operational stage, PED is applied to identify the predefined sequences of system calls invoked by running processes and estimates their energy consumption.

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DevScope

Cited by 79 publications

References 10 publications

Power profiling and monitoring in embedded systems: A comparative study and a novel methodology based on NARX neural networks

Power profiling and monitoring in embedded systems: A comparative study and a novel methodology based on NARX neural networks

Incremental Modeling and Monitoring of Embedded CPU-GPU Chips

A Machine Learning Method for Power Prediction on the Mobile Devices

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