Artificial Intelligence in CPU and ASIC Design: Applications, Design-Space Exploration, and Engineering Constraints

Abstract

Modern CPU and application-specific integrated circuit (ASIC) development requires engineers to search large design spaces while satisfying power, performance, area, timing, and correctness constraints. Artificial intelligence (AI) is increasingly being integrated into electronic design automation (EDA) to accelerate prediction, optimization, physical design, and hardware-description generation. This paper reviews how graph neural networks, reinforcement learning, and large language or generative models can support CPU and ASIC development. It connects these techniques to architectural exploration, RTL generation, logic synthesis, placement, and verification, while emphasizing that learned models do not replace deterministic sign-off. Evidence from recent EDA research shows that AI can reduce search cost and automate selected design tasks, but correctness, data quality, model generalization, and verification remain major engineering constraints. The most practical near-term model is therefore a hybrid AI-EDA workflow in which AI proposes and prioritizes alternatives while conventional tools and engineers validate the final design.