Machine Learning Applications in Electronic Design Automation

This book is a single-source reference for key machine learning (ML) applications and methods in digital and analog design and verification, covering analysis and optimization of digital and analog designs, functional verification, FPGA and system-level designs, design for manufacturing (DFM), and design space exploration. It also covers key ML methods such as classical ML, deep learning models, GNNs, GANs, and optimization methods.

Format: Hardback
Length: 583 pages
Publication date: 02 January 2023
Publisher: Springer International Publishing AG

​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.  

Weight: 1051g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031130731
Edition number: 1st ed. 2022