Titlebook: Machine Learning Applications in Electronic Design Automation; Haoxing Ren,Jiang Hu Book 2022 The Editor(s) (if applicable) and The Author

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https://doi.org/10.1007/978-3-031-13074-8Deep learning for EDA; convolutional neural networks; graph neural networks; generative adversarial net

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deep learning models such as convolutional neural networks .?This book serves as a single-source reference to key machine learning (ML) applications and methods in digital and analog design and verification. Experts from academia and industry cover a wide range of the latest research on ML applicat

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Book 2022 convolutional neural networks (CNNs), graph neural networks (GNNs), generative adversarial networks (GANs) and optimization methods such as reinforcement learning (RL) and Bayesian optimization (BO). All of these topics are valuable to chip designers and EDA developers and researchers working in digital and analog designs and verification.??.

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Deep Learning Framework for Placementmeworks to accelerate kernel placement solvers as well as integrating machine learning models to speed up cross-layer optimization. We hope this line of studies can broaden the applications of machine learning techniques in IC design automation and stimulate more researches in related fields.

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Machine Learning in the Service of Hardware Functional Verificationconnect the various verification tools and data sources and create a unified data repository in a data warehouse that is optimized for data retrieval. This connection allows the use of advanced data science techniques that improve the quality of the entire verification process.

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