جستجوی مقالات مرتبط با کلیدواژه "مدیریت دمای پویا" در نشریات گروه "فنی و مهندسی"

Increasing the number of cores to enhance computing power of processors leads to an increase in temperature for multicore systems. Thermal management is significant challenge in these processors. A proactive dynamic thermal management uses a thermal model to predict the temperature before processor temperature reaches the threshold. In this paper, some appropriate features for thermal model are read by using system measurement tools. Other features as historical and control features are created using the proposed processes. An online thermal model based on several thermal phase is proposed. For each phase, a neural network is used to forecast temperature. Different thermal phases are identified according to the parameters affecting the processor temperature using the adaptive resonance theory network. For each of the neural networks, the minimum number of proper features is selected based on the mutual information between the features. The proposed thermal model is able to detect new thermal phase at run time. Then, appropriate neural network is created for new phase. The proposed model has been evaluated to predict temperature for different time distances. The results shows the mean absolute error is less than 1 °C.

Microprocessor designers use the design of multicore systems on a chip to increase their computing power. Adding the number of cores leads to an increase in the power density, followed by enhancement of temperature. Reactive and proactive approaches are two sets of the methods for managing the temperature. Unlike the reactive methods that act based on threshold temperature, proactive approaches utilize a thermal prediction model in thermal management. In this paper, two multilayer perceptron neural networks has been used for thermal prediction and temperature control. An appropriate dataset is provided for training each model. This dataset consists of some features that are read by sensors and measurement tools and new features that are produced by proposed processes. In this regard, historical features are suggested for thermal model. Proper features of thermal model are selected by using feature selection based on mutual information. The temperature is controlled by setting the processor frequency and fan speed. The features of control model are selected by non-dominated sorting genetic algorithm. The error of thermal model for different time distances is about 0.5 °C. The thermal control model has respectively 2% and 0.6% errors in determining the processor frequency and fan speed.

Increasing the number of processor cores leads to increasing the density of the computing power processor and also raising the temperature. Temperature management is very important in these processors. Thermal management methods are introduced to reduce the CPU temperature. Reactive and proactive approaches are two sets of these schemes. Unlike the reactive techniques, proactive methods predict the temperature using thermal prediction model before reaching its threshold. In this paper, a hybrid model of several SVR models is proposed for predicting temperature. An appropriate dataset is created for training proposed model that includes a high diversity of processor temperature variations. Some features of dataset are measured using temperature sensors and system performance counters. Other features, with historical and control names are calculated with the proposed processes to increase the accuracy of thermal model. Two SVR models are used in the proposed thermal model to reduce its operational overhead. The proper features for each SVR model are selected by the feature selection algorithm based on mutual information. The proposed model is evaluated for temperature prediction for 2 to 5 time distances. The results show that with a selection of 11 features for thermal prediction model of the next 2 seconds, the mean absolute error is about 0.5 °C.