Session

DescriptionThis session explores systems that use multi-agent coordination, generative models, and learning-driven optimization to automate tasks ranging from PCB and trace generation to CUDA kernel evolution and HLS design exploration. The papers also introduce new benchmarking and infrastructure approaches that allow AI-driven EDA tools to iteratively improve and evaluate themselves within complex design flows.