@article{1452358,
      note = {Includes index.},
      author = {Ren, Haoxing. and Hu, Jiang.},
      url = {http://library.usi.edu/record/1452358},
      title = {Machine learning applications in electronic design  automation /},
      abstract = {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 applications in electronic design automation (EDA),  including analysis and optimization of digital design,  analysis and optimization of analog design, as well as  functional verification, FPGA and system level designs,  design for manufacturing (DFM), and design space  exploration. The authors also cover key ML methods such as  classical ML, deep learning models such as 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. Serves as a  single-source reference to key machine learning (ML)  applications and methods in digital and analog design and  verification; Covers classical ML methods, as well as deep  learning models such as convolutional neural networks  (CNNs), graph neural networks (GNNs), generative  adversarial networks (GANs) and optimization methods such  as reinforcement learning (RL) and Bayesian optimization  (BO); Discusses machine learning MLs applications in  electronic design automation (EDA), especially in the  design automation of VLSI integrated circuits.},
      doi = {https://doi.org/10.1007/978-3-031-13074-8},
      recid = {1452358},
      pages = {1 online resource (1 volume) :},
}