Towards collaborative intelligence: routability estimation based on decentralized private data
Towards collaborative intelligence: routability estimation based on decentralized private data
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DOI：
10.1145/3489517.3530578
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Jingyu Pan;Chen-Chia Chang;Zhiyao Xie;Ang Li;Minxue Tang;Tunhou Zhang;Jiangkun Hu;Yiran Chen
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文献类型：
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作者：
Jingyu Pan;Chen-Chia Chang;Zhiyao Xie;Ang Li;Minxue Tang;Tunhou Zhang;Jiangkun Hu;Yiran Chen
在设计流程中应用机器学习（ML）是电子设计自动化（EDA）的一个流行趋势，具有从设计质量预测到优化的各种应用。尽管它的前景已经在学术研究和工业工具中得到了证明，但它的有效性在很大程度上取决于大量高质量训练数据的可用性。实际上，EDA开发人员对最新设计数据的访问非常有限，这些数据由设计公司所有，并且大多是机密的。尽管可以将ML模型训练委托给设计公司，但单个公司的数据可能仍然不充分或存在偏见，特别是对于小公司。这样的数据可用性问题正在成为限制芯片设计ML未来增长的限制因素。在这项工作中，我们提出了一种基于联邦学习的方法，用于EDA中的ML应用程序。我们的方法允许ML模型使用来自多个客户端的数据进行协作训练，但无需显式访问数据以尊重其数据隐私。为了进一步加强结果，我们共同设计了一个定制的ML模型FLNet及其在分散训练场景下的个性化。在综合数据集上的实验表明，与单个局部模型相比，协作训练的准确率提高了11%，并且我们的定制模型FLNet在这种协作训练流程中的性能明显优于以前最好的可路由性估计器。
Applying machine learning (ML) in design flow is a popular trend in Electronic Design Automation (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.