Energy-Efficient Scheduling Based on Task Migration Policy Using DPM for Homogeneous MPSoCs

Increasing the life span and efficiency of Multiprocessor System on Chip(MPSoC)by reducing power and energy utilization has become a critical chip design challenge for multiprocessor systems.With the advancement of technology,the performance management of central processing unit(CPU)is changing.Power densities and thermal effects are quickly increasing in multi-core embedded technologies due to shrinking of chip size.When energy consumption reaches a threshold that creates a delay in complementary metal oxide semiconductor(CMOS)circuits and reduces the speed by 10%–15%because excessive on-chip temperature shortens the chip’s life cycle.In this paper,we address the scheduling&energy utilization problem by introducing and evaluating an optimal energy-aware earliest deadline first scheduling(EA-EDF)based technique formultiprocessor environments with task migration that enhances the performance and efficiency in multiprocessor systemon-chip while lowering energy and power consumption.The selection of core andmigration of tasks prevents the system from reaching itsmaximumenergy utilization while effectively using the dynamic power management(DPM)policy.Increase in the execution of tasks the temperature and utilization factor(u_(i))on-chip increases that dissipate more power.The proposed approach migrates such tasks to the core that produces less heat and consumes less power by distributing the load on other cores to lower the temperature and optimizes the duration of idle and sleep times across multiple CPUs.The performance of the EA-EDF algorithm was evaluated by an extensive set of experiments,where excellent results were reported when compared to other current techniques,the efficacy of the proposed methodology reduces the power and energy consumption by 4.3%–4.7%on a utilization of 6%,36%&46%at 520&624 MHz operating frequency when particularly in comparison to other energy-aware methods for MPSoCs.Tasks are running and accurately scheduled to make an energy-efficient processor by controlling and managing the thermal effects on-chip and optimizing the energy consumption of MPSoCs.

An Optimal DPM Based Energy-Aware Task Scheduling for Performance Enhancement in Embedded MPSoC

Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip(MPSoC)has become an integral chip design issue for multiprocessor systems.The performance measurement of computational systems is changing with the advancement in technology.Due to shrinking and smaller chip size power densities onchip are increasing rapidly that increasing chip temperature in multi-core embedded technologies.The operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor(CMOS)circuits because high on-chip temperature adversely affects the life span of the chip.In this paper an energy-aware dynamic power management technique based on energy aware earliest deadline first(EA-EDF)scheduling is proposed for improving the performance and reliability by reducing energy and power consumption in the system on chip(SOC).Dynamic power management(DPM)enables MPSOC to reduce power and energy consumption by adopting a suitable core configuration for task migration.Task migration avoids peak temperature values in the multicore system.High utilization factor(ui)on central processing unit(CPU)core consumes more energy and increases the temperature on-chip.Our technique switches the core bymigrating such task to a core that has less temperature and is in a low power state.The proposed EA-EDF scheduling technique migrates load on different cores to attain stability in temperature among multiple cores of the CPU and optimized the duration of the idle and sleep periods to enable the low-temperature core.The effectiveness of the EA-EDF approach reduces the utilization and energy consumption compared to other existing methods and works.The simulation results show the improvement in performance by optimizing 4.8%on u_(i) 9%,16%,23%and 25%at 520 MHz operating frequency as compared to other energy-aware techniques for MPSoCs when the least number of tasks is in running state and can schedule more tasks to make an energy-efficient processor by controlling and managing the energy consumption of MPSoC.

Rigid Sensor Allocation and Placement Technique for Reducing the Number of Sensors in Thermal Monitoring

Using embedded thermal sensors, dynamic thermal management(DTM) techniques measure runtime thermal behavior of high-performance microprocessors so as to prevent thermal runaway situations. The number of placed sensors should be minimized, while guaranteeing accurate tracking of hot spots and full thermal characterization. In this paper, we propose a rigid sensor allocation and placement technique for determining the minimal number of thermal sensors and the optimal locations while satisfying an expected accuracy of hot spot temperature error based on dual clustering. We analyze the false alarm rates of hot spots using the proposed methods in noise-free, with noise and sensor calibration scenarios, respectively. Experimental results confirm that our proposed methods are capable of accurately characterizing the temperatures of microprocessors.

Surface Spline Interpolation Method for Thermal Reconstruction with Limited Sensor Data of Non-Uniform Placements

With the characteristic size reducing as well as the power densities exponentially increasing, elevated chip temperatures are true limiters to the performance and reliability of integrated circuits. To address these thermal issues, it is essential to use a set of on-chip thermal sensors to monitor temperatures during operation.These temperature sampling results are then used by thermal management techniques to appropriately manage chip performance. In this paper, we propose a surface spline interpolation method to reconstruct the full thermal characterization of integrated circuits with non-uniform thermal sensor placements. We construct the thermal surface function using the mathematical tool of surface spline with the matrix calculation of the non-uniform sample data. Then, we take the coordinates of the points at grid locations into the surface function to get its temperature value so that we can reconstruct the full thermal signals. To evaluate the effiectiveness of our method,we develop an experiment for reconstructing full thermal status of a 16-core processor. Experimental results show that our method outperforms the inverse distance weighting method based on dynamic Voronoi diagram and spectral analysis techniques both in the average absolute error metric and the hot spot absolute error metric with short enough runtime to meet the real-time process demand. Besides, our method still has the advantages such as its mathematical simplicity with no need of pre-process.