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<p>Artificial intelligence (AI) is becoming increasingly relevant in many contexts. In embedded real-time systems, most of the previous research has focused on real-time guarantees for AI workloads (RT-for-AI). Instead, this position paper discusses the potential benefits and application cases of the complementary direction of using AI to optimize real-time systems themselves (AI-for-RT). It presents a vision where AI techniques, such as supervised and reinforcement learning, support system design and online configuration activities that are traditionally addressed using Mixed-Integer Linear Programming (MILP) or heuristic methods. The paper discusses scenarios where AI can potentially outperform classical techniques—such as recursive real-time analysis, systems with complex hardware/software interactions, and dynamic resource management—highlighting the promise of AI in both design-time and runtime real-time systems optimization. Solutions are left to future work: the goal is to populate the “Roadmap Towards Learning-Enabled and Learning-Assisted Real-Time Systems”, which is the target of this special issue.</p>

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To MILP or not to MILP? On AI techniques for the design and optimization of real-time systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月26日

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  • Daniel Casini

摘要

Artificial intelligence (AI) is becoming increasingly relevant in many contexts. In embedded real-time systems, most of the previous research has focused on real-time guarantees for AI workloads (RT-for-AI). Instead, this position paper discusses the potential benefits and application cases of the complementary direction of using AI to optimize real-time systems themselves (AI-for-RT). It presents a vision where AI techniques, such as supervised and reinforcement learning, support system design and online configuration activities that are traditionally addressed using Mixed-Integer Linear Programming (MILP) or heuristic methods. The paper discusses scenarios where AI can potentially outperform classical techniques—such as recursive real-time analysis, systems with complex hardware/software interactions, and dynamic resource management—highlighting the promise of AI in both design-time and runtime real-time systems optimization. Solutions are left to future work: the goal is to populate the “Roadmap Towards Learning-Enabled and Learning-Assisted Real-Time Systems”, which is the target of this special issue.