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<p>Real-time scheduling ensures predictability in computing systems, ensuring that tasks meet stringent timing constraints. The integration of machine learning and neural networks into real-time scheduling offers new paradigms for solving constrained optimization problems. This paper briefly explores the intersection of machine learning and real-time scheduling, covering the role of neurodynamic systems, reinforcement learning, and the application of real-time constraints to machine learning models. Additionally, the study discusses challenges in implementing real-time machine learning, including system architectures, accelerators, and safety concerns. The paper concludes with insights into our recent advancements in wearable healthcare systems and secure neural networks in real-time environments. </p>

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When machine learning and neural networks marry real-time scheduling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月1日

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  • Zhishan Guo

摘要

Real-time scheduling ensures predictability in computing systems, ensuring that tasks meet stringent timing constraints. The integration of machine learning and neural networks into real-time scheduling offers new paradigms for solving constrained optimization problems. This paper briefly explores the intersection of machine learning and real-time scheduling, covering the role of neurodynamic systems, reinforcement learning, and the application of real-time constraints to machine learning models. Additionally, the study discusses challenges in implementing real-time machine learning, including system architectures, accelerators, and safety concerns. The paper concludes with insights into our recent advancements in wearable healthcare systems and secure neural networks in real-time environments.