ACM/IEEE 8th Symposium on Machine Learning for CAD (MLCAD)

This symposium seeks original submissions on the application of ML to the entire design, validation, and test flow. We encourage contributions that leverage state-of-the-art ML techniques, including but not limited to Large Language Models (LLMs), agentic AI, Reinforcement Learning (RL), Graph Neural Networks (GNNs), and Generative Models (e.g., Diffusion, GANs), to address critical bottlenecks in CAD. Recent years have seen active research in areas such as ML-powered layout generation, mask preparation, and enhancing the robustness and reliability of integrated circuits. Furthermore, with power and thermal management being critical limiting factors for modern ICs, ML-based techniques offer promising solutions. In addition to submissions from academia, submissions from industry are much welcome.

ACM/IEEE 8th Symposium on Machine Learning for CAD (MLCAD) is technically sponsored by IEEE. The proceedings  of this event are likely to be indexed in  prominent databases such as Scopus, Web of Science (WoS), Ei Compendex, DBLP, Google Scholar, and many others.