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A Hopfield neural network approach for power optimization of real-time operating systems

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Abstract

The RTOS (Real-Time Operating System) is a critical component in the SoC (System-on-a-Chip), which is the main body for consuming total system energy. Power optimization based on hardware–software partitioning of a RTOS (RTOS–Power partitioning) can significantly minimize the energy consumption of a SoC. This paper presents a new model for RTOS–Power partitioning, which helps in understanding the essence of the RTOS–Power partitioning techniques. A discrete Hopfield neural network approach for implementing the RTOS–Power partitioning is proposed, where a novel energy function, operating equation and coefficients of the neural network are redefined. Simulations are carried out with comparison to other optimization techniques. Experimental results demonstrate that the proposed method can achieve higher energy savings up to 60% at relatively low costs of less than 4k PLBs while increasing the performance compared to the purely software realized SoC–RTOS.

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Acknowledgments

The authors thank the anonymous reviewers for their constructive comments, which helped us in improving the quality of this paper. The first author acknowledges the financial support from the National Natural Science Foundation of China under Grant no. 60572026.

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Authors and Affiliations

  1. School of Computer Science and Engineering, SiChuan University, Chengdu, 610065, China

    Bing Guo & Zhi Shu Li

  2. Department of Computer Science and Engineering, La Trobe University, Melbourne, VIC, 3086, Australia

    Dian Hui Wang

  3. School of Mechatronics Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Yan Shen

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  1. Bing Guo
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  2. Dian Hui Wang
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  3. Yan Shen
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  4. Zhi Shu Li
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Correspondence to Bing Guo.

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Guo, B., Wang, D.H., Shen, Y. et al. A Hopfield neural network approach for power optimization of real-time operating systems. Neural Comput & Applic 17, 11–17 (2008). https://doi.org/10.1007/s00521-006-0074-6

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  • DOI: https://doi.org/10.1007/s00521-006-0074-6

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