Thermal Runaway in Integrated Circuits

Vassighi, A.; Sachdev, Manoj

doi:10.1109/tdmr.2006.876577

Cited by 91 publications

(80 citation statements)

References 5 publications

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“…To develop the thermal RC network model, the thermal resistance and capacitance values are characterized by multi-parameter fitting [6] based on the observation that AP is a dominant dynamic heat source in the mobile devices. According to the thermal network modeling in [7,8], the temperature at the beginning of the PCB can be determined by the power passing through the surface components and the surface temperature. The correlation between the PCB temperature (T PCB ) and the surface temperature (T surface ) can be described as in Eq.…”

Section: Ap Power Estimationmentioning

confidence: 99%

Surface temperature-aware thermal management technique for mobile devices

Yim

Lee

Han

2014

IEICE Electron. Express

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Recent demand on more advanced functionalities in mobile devices has led to a drastic increase in power consumption of the device. Therefore, heat dissipation has become a major concern. To effectively overcome the thermal barrier based on the surface temperature of mobile devices, we propose a surface temperature-aware thermal management (STaTM) technique to be incorporated into a mobile application processor (AP), which plays a key role in the heat management of the mobile device. The proposed STaTM enables the AP to maximize the energy efficiency using accuracy-enhanced rapid surface temperature estimation. Experimental results show that the proposed solution achieves an average performance improvement of 14.2% when compared to conventional thermal management techniques.

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Section: Ap Power Estimationmentioning

confidence: 99%

Surface temperature-aware thermal management technique for mobile devices

Yim

Lee

Han

2014

IEICE Electron. Express

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“…For instance, since Aceplorer does not model the system functional behavior, it needs an execution scenario, generated from a functional system simulation, to activate the elements of its power model and consequently evaluate the power consumption of the system. In the same way, Hotspot is based on the Compact Thermal Model [22], which defines the model of a the thermal features of the system component and their thermal influence over their neighbors. This model does not represent the system activity needed by Hotspot to generate the temperature layout of the system.…”

Section: B Non-functional (Thermal) Modelingmentioning

confidence: 99%

Execution of heterogeneous models for thermal analysis with a multi-view approach

Khecharem

Gómez

Deantoni

et al. 2014

Proceedings of the 2014 Forum on Specification and Design Languages (FDL)

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Abstract-To deal with the high complexity of embedded systems, engineers rely on high-level heterogeneous models that combine functional and non-functional aspects, hardware/software artifacts, structural and behavioral descriptions. PRISMSYS is a system-level multi-view modeling framework, which provides a means to specify functional and non-functional aspects in interrelated views. Each concern/view is addressed separately with a dedicated set of models and correspondence rules, maintaining the semantic consistency between those different views. The behavioral specification mixes UML state machines with equational models defined as SYSML parametric diagrams. To supply a complete non-functional property-aware simulation environment, it is mandatory to formalize 1) the execution semantics of the UML state machines, 2) the SYSML parametric diagrams and 3) the coordination between them. This is achieved by using CCSL, the Clock Constraint Specification Language, to provide an event-based semantics for each model and their coordination. The proposed co-simulation framework combines TIMESQUARE, a discrete event simulator for CCSL, and Scilab, a tool for numerical computation. The framework is illustrated on a CPU thermal manager case study with a joint simulation of both its functional and non-functional models.

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“…The optimization of fitness function F(T, A, P, L) requires the optimization of the four objective functions. It has been shown that these objective functions are conflicting in nature (Vassighi and Sachdev 2006;Suwa and Hadim 2007;Masana 2007). In order to use the weighted sum approach (Hajela and Lin 1992;Murata and Ishibuchi 1995), these objective functions can be combined into a scalar fitness function given in Eq.…”

Section: Multi-objective Problems Formulationmentioning

confidence: 99%

Optimization of electronics component placement design on PCB using self organizing genetic algorithm (SOGA)

2010

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The optimal placement of electronic components on a printed circuit board (PCB) requires satisfying multiple conflicting design objectives as most of the components have different power dissipation, operating temperature, types of material and dimension. In addition, most electronic companies are currently emphasizing on designing a smaller package electronic system in order to increase the system performance. This paper presents a new self organizing genetic algorithm (SOGA) method for solving this multi-objective optimization problem. The SOGA can be viewed as a cascade of two GAs which consists of two steps fitness evaluation process to ensure that the fitness of selected chromosomes for each iteration process is optimally selected. The algorithm is developed based on weighted sum approach genetic algorithm (WSGA) where an inner loop GA is used to optimize the selection of weights of the WSGA. Experiments are conducted to evaluate the performance of SOGA. Four objective functions are formulated in the experiments which are temperature of components, area of PCB, high power component placement and high potential critical components distance. Comparisons of the performance of SOGA are made with two well known methods namely fixed weight GA (FWGA) and random weighted GA (RWGA). The results show that the SOGA gives a better optimal solution as compared to the other methods.

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Thermal Runaway in Integrated Circuits

Cited by 91 publications

References 5 publications

Surface temperature-aware thermal management technique for mobile devices

Surface temperature-aware thermal management technique for mobile devices

Execution of heterogeneous models for thermal analysis with a multi-view approach

Optimization of electronics component placement design on PCB using self organizing genetic algorithm (SOGA)

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