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  • Open Access


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

    Hamayun Khan1,*, Irfan Ud Din2, Arshad Ali3, Mohammad Husain3

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2097-2113, 2023, DOI:10.32604/cmc.2023.032999

    Abstract 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 on-chip 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… More >

  • Open Access


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

    Hamayun Khan1,*, Irfan Ud din2, Arshad Ali3, Sami Alshmrany3

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 965-981, 2023, DOI:10.32604/cmc.2023.031223

    Abstract 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… More >

  • Open Access


    Realization of Deep Learning Based Embedded Soft Sensor for Bioprocess Application

    V. V. S. Vijaya Krishna1,*, N. Pappa1, S. P. Joy Vasantharani2

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 781-794, 2022, DOI:10.32604/iasc.2022.022181

    Abstract Industries use soft sensors for estimating output parameters that are difficult to measure on-line. These parameters can be determined by laboratory analysis which is an offline task. Now a days designing Soft sensors for complex nonlinear systems using deep learning training techniques has become popular, because of accuracy and robustness. There is a need to find pertinent hardware for realizing soft sensors to make it portable and can be used in the place of general purpose PC. This paper aims to propose a new strategy for realizing a soft sensor using deep neural networks (DNN) on appropriate hardware which can… More >

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