Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data

TL;DR

This paper introduces a federated learning approach for EDA design tasks, enabling collaborative model training across companies without sharing sensitive data, leading to improved accuracy and better routability estimation.

Contribution

It proposes a novel federated learning framework and a customized ML model, FLNet, for privacy-preserving collaborative EDA model training, outperforming existing estimators.

Findings

Collaborative training improves accuracy by 11%.

FLNet outperforms previous routability estimators.

Decentralized training enhances model performance.

Abstract

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.