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Machine learning based workload balancing scheme for minimizing stress migration induced aging in multicore processors

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Abstract

Stress migration-induced lifetime reliability is an important aging concern in modern embedded devices designed with high-performance multi-core processors. Due to the constraints in area requirements, these processing cores are manufactured with a high level of integration. These densely integrated processors, when operated continuously at elevated temperatures, accelerate processor wear-out. Since stress migration has a strong dependence on temperature, accurate and computationally efficient thermal estimation is important to take real-time control action to ensure that the chip lifetime meets the requirements. This research work consists of two sections. In the first part of this work, we propose a thermal estimation model based on the K-Nearest Neighbor machine learning algorithm for the significant energy-consuming elements of a processor core. In the second part, an aging-aware scheduler is proposed which can do workload assignment and processor adaptation based on the aging estimation performed with the developed thermal model to improve processor lifetime. According to experimental findings, the suggested system is accurate, efficient, and suitable for real-time implementation.

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  1. Department of Electronics Engineering, Government Model Engineering College, Thrikkakara, Cochin 21, India

    P. Jagadeesh Kumar & M. G. Mini

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Kumar, P.J., Mini, M.G. Machine learning based workload balancing scheme for minimizing stress migration induced aging in multicore processors. Int. j. inf. tecnol. 15, 399–410 (2023). https://doi.org/10.1007/s41870-022-01105-6

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