ThermTap: An online power analyzer and thermal simulator for Android devices

Dousti, Mohammad Javad; Ghasemi-Gol, Majid; Nazemi, Mahdi; Pedram, Massoud

doi:10.1109/islped.2015.7273537

Cited by 14 publications

(11 citation statements)

References 9 publications

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“…HotSpot [19] is one of the most popular compact thermal modeling techniques for a system which consists of various hardware components. Many researchers have adapted and applied it for simulating thermal behaviors of their target systems, but only a few studies targeted mobile devices [6][7][8][9]11,12].…”

Section: Thermal Prediction Methods For Mobile Devicesmentioning

confidence: 99%

“…Xie et al [6] proposed a CTM-based thermal model considering the thermal interaction between mobile AP (Application Processor) and battery. Dousti et al [7] proposed a CTM-based thermal analyzer, which produces various thermal maps for major hardware components such as CPU, GPU, LCD, WiFi, 4G LTE, and flash storage in a mobile device. Also, authors in [8,9,11] used similar CTM-based thermal models for thermal prediction.…”

Section: Thermal Prediction Methods For Mobile Devicesmentioning

confidence: 99%

“…The speed of the temperature change, i.e., dT(t) dt , is reversely proportional to the thermal capacitance C T . This 1-node thermal RC circuit is extended to an n-node thermal RC network by several studies [6,7,26] to model the thermal behaviors of multiple CPU cores, LCD, WiFi chipset, and other hardware components in a smartphone device. Figure 6b shows an example of a 4-node thermal RC network where four 1-node thermal RC circuits are completely interconnected by inter-node thermal resistances, which models thermal interactions among hardware components through their interconnecting materials.…”

Section: Extension Of Existing Thermal Modelmentioning

confidence: 99%

“…To protect microprocessors from thermal violations, there have been many studies on thermal prediction-based DTM (Dynamic Thermal Management) [6][7][8][9][10][11][12][13][14][15][16] to predict the temperature and then to lower the resource usage if the predicted temperature is at risk of exceeding the thermal threshold. Since those studies consider rapid temperature increases as abnormal and rare cases, they use simple solutions despite the risk of thermal violations.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Planning of Dynamic Thermal Management for NANS (N-App N-Screen) Services

et al. 2018

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Existing multi-screening technologies have been limited to mirroring the current screen of the smartphone onto all the connected external display devices. In contrast, NANS (N-App N-Screen) technology is able to display different applications (N-App) on different multiple display devices (N-Screen) using only a smartphone. For such NANS services, this paper empirically shows that the thermal violation constraint is more critical than the battery life constraint. For preventing the thermal violation, the existing DTM (Dynamic Thermal Management) techniques cannot be used since they consider thermal violations as abnormal, and hence prevent them by severely throttling CPU frequencies resulting in serious QoS degradation. In NANS service scenarios it is normal to operate in high temperature ranges to continue services with acceptable QoS. Targeting such scenarios, we first propose a novel thermal prediction method specially designed for NANS services. Based on the novel thermal prediction method, we then propose a novel DTM technique called, “thermal planning” to provide sustainable NANS services with sufficiently high QoS without thermal violations.

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Section: Thermal Prediction Methods For Mobile Devicesmentioning

confidence: 99%

Section: Thermal Prediction Methods For Mobile Devicesmentioning

confidence: 99%

Section: Extension Of Existing Thermal Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Planning of Dynamic Thermal Management for NANS (N-App N-Screen) Services

et al. 2018

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“…The closest work to our framework-models used online estimation and monitoring-is the Therminator [47] simulator and its second version ThermTap [48]. These programs are developed for the online estimation of temperature in mobile devices for debugging purposes.…”

Section: Temperature Modeling In Embeded 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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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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ThermTap: An online power analyzer and thermal simulator for Android devices

Cited by 14 publications

References 9 publications

Optimal Planning of Dynamic Thermal Management for NANS (N-App N-Screen) Services

Optimal Planning of Dynamic Thermal Management for NANS (N-App N-Screen) Services

Incremental Modeling and Monitoring of Embedded CPU-GPU Chips

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

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