Session

DescriptionThe integration of generative AI into EDA is ushering in a new era of hardware innovation. This session explores cutting-edge advancements in the application of machine learning models, particularly Large Language Models (LLMs), to hardware design automation and quantum code generation. The session begins with presentations on automating hardware design using LLMs. The discussion shifts to the potential of foundation models in enhancing EDA, followed by a novel approach to quantum code generation through LLMs. The session further delves into the use of diffusion models for generating synthetic RTL circuits, and concludes with the introduction of a multi-layered, hierarchical dataset to support machine learning in hardware design.