New directions for learning-based IC design tools and methodologies

Design-based equivalent scaling now bears much of the burden of continuing the semiconductor industry's trajectory of Moore's-Law value scaling. In the future, reductions of design effort and design schedule must comprise a substantial portion of this equivalent scaling. In this context, machine learning and deep learning in EDA tools and design flows offer enormous potential for value creation. Examples of opportunities include: improved design convergence through prediction of downstream flow outcomes; margin reduction through new analysis correlation mechanisms; and use of open platforms to develop learning-based applications. These will be the foundations of future design-based equivalent scaling in the IC industry. This paper describes several near-term challenges and opportunities, along with concrete existence proofs, for application of learning-based methods within the ecosystem of commercial EDA, IC design, and academic research.