AI-Driven Chip Design Automation Using Large Language Models and Reinforcement Learning
Keywords:
chip design automation; large language models; reinforcement learning; electronic design automation; socio-technical systems; AI governance; robustness; sustainabilityAbstract
The design of modern integrated circuits has become an increasingly complex socio-technical undertaking in which architectural ambition, manufacturing constraints, verification burden, and economic pressure interact across global supply chains. This paper examines artificial intelligence driven chip design automation through the combined use of large language models and reinforcement learning. It argues that neither language models nor reinforcement learning alone can resolve the structural bottlenecks of electronic design automation. Large language models are well suited to reasoning over heterogeneous design knowledge, specifications, documentation, scripts, and code, but they remain probabilistic, difficult to verify, and vulnerable to hallucination. Reinforcement learning can optimize sequential decisions in placement, routing, scheduling, and resource allocation, yet it depends on costly simulators, fragile reward functions, and extensive exploration. The paper develops a system-level architecture that treats language models as interfaces and proposal generators, reinforcement learning as an optimization and control layer, and conventional electronic design automation tools as authoritative verifiers. It analyzes deployment trade-offs across cloud and on-premise infrastructure, data governance, model lifecycle management, energy consumption, robustness, security, fairness, and policy. The discussion emphasizes human oversight, reproducibility, benchmark design, and institutional accountability. The conclusion is that AI-driven chip design should be understood not as a single model replacing engineers but as a governed infrastructure in which learning systems, formal tools, human expertise, and public standards co-produce trustworthy designs. Such a perspective is necessary for sustainable adoption across commercial, academic, and national semiconductor ecosystems.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.