AI for Chip Design

The design of modern semiconductor chips is one of the most complex intellectual and engineering challenges in computer science, and one of the last frontiers where AI has yet to deliver transformative impact at scale. Recent years have seen a surge of machine learning contributions across the chip design stack, from graph neural networks for routing and timing prediction, to large language models for hardware description languages, to agentic systems navigating full EDA workflows. Yet the communities driving these advances remain fragmented: ML researchers rarely attend hardware venues, and EDA practitioners have limited exposure to frontier ML methods. This workshop brings both communities together at NeurIPS to share results, identify open problems, and build the interdisciplinary research agenda that AI for chip design urgently needs. We invite contributions spanning all ML paradigms and all stages of the chip design flow, from specification to tapeout.