Applying machine learning (ML) in design flow is a popular trend in EDA with
various applications from design quality predictions to optimizations. Despite
its promise, which has been demonstrated in both academic researches and
industrial tools, its effectiveness largely hinges on the availability of a
large amount of high-quality training data. In reality, EDA developers have
very limited access to the latest design data, which is owned by design
companies and mostly confidential. Although one can commission ML model
training to a design company, the data of a single company might be still
inadequate or biased, especially for small companies. Such data availability
problem is becoming the limiting constraint on future growth of ML for chip
design. In this work, we propose an Federated-Learning based approach for
well-studied ML applications in EDA. Our approach allows an ML model to be
collaboratively trained with data from multiple clients but without explicit
access to the data for respecting their data privacy. To further strengthen the
results, we co-design a customized ML model FLNet and its personalization under
the decentralized training scenario. Experiments on a comprehensive dataset
show that collaborative training improves accuracy by 11% compared with
individual local models, and our customized model FLNet significantly
outperforms the best of previous routability estimators in this collaborative
training flow.Comment: 6 pages, 2 figures, 5 tables, accepted by DAC'2