Q‐scheduler: A temperature and energy‐aware deep Q‐learning technique to schedule tasks in real‐time multiprocessor embedded systems
نویسندگان
چکیده
Reducing energy consumption under processors' temperature constraints has recently become a pressing issue in real-time multiprocessor systems on chips (MPSoCs). The high of processors affects the power and reliability MPSoC. Low is necessary for embedded systems, as most them are portable devices. Efficient task mapping significant impact reducing thermal profile processors. Several state-of-the-art techniques have been proposed this issue. This paper proposes Q-scheduler, novel technique based deep Q-learning technology, to dispatch tasks between Thousands simulated train Q-scheduler offline reduce system's trained dispatches real MPSoC online while also being regularly online. multiple system simultaneously with single process; effectiveness ability significant, especially harmonic system. Experimental results illustrate that reduces average by 15% 10%, respectively, compared previous techniques.
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ژورنال
عنوان ژورنال: Iet Computers and Digital Techniques
سال: 2022
ISSN: ['1751-861X', '1751-8601']
DOI: https://doi.org/10.1049/cdt2.12044