Machine learning applications in electronic design automation / Haoxing Ren, Jiang Hu, editors.
Ren, Haoxing. editor.; Hu, Jiang. editor.
2022
TK7867 .M33 2022
Available Online

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Title Machine learning applications in electronic design automation / Haoxing Ren, Jiang Hu, editors.
ISBN 9783031130748 electronic book
303113074X electronic book
3031130731
9783031130731
DOI https://doi.org/10.1007/978-3-031-13074-8
Published Cham, Switzerland : Springer, 2022.
Language English
Description 1 online resource (1 volume) : illustrations (black and white, and colour).
Item Number 10.1007/978-3-031-13074-8 doi
Call Number TK7867 .M33 2022
Dewey Decimal Classification 621.38150285/63
Summary 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.
Note Includes index.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Ren, Haoxing. editor.
Hu, Jiang. editor.
Available in Other Form MACHINE LEARNING APPLICATIONS IN ELECTRONIC DESIGN AUTOMATION.
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Introduction
Analysis of Digital Design: Routability Optimization for Industrial Designs at Sub-14nm Process Nodes Using Machine Learning
RouteNet: Routability Prediction for Mixed-size Designs Using Convolutional Neural Network
High Performance Graph Convolutional networks with Applications in Testability Analysis
MAVIREC: ML-Aided Vectored IR-Drop Estimation and Classification
GRANNITE: Graph Neural Network Inference for Transferable Power Estimation
Machine Learning-Enabled High-Frequency Low-Power Digital Design Implementation at Advanced Process Nodes
Optimization of Digital Design: Chip Placement with Deep Reinforcement learning
DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement
TreeNet: Deep Point Cloud Embedding for Routing Tree Construction
Asynchronous Reinforcement Learning Framework for Net Order Exploration in Detailed Routing
Standard Cell Routing with Reinforcement Learning and Genetic Algorithm in Advanced Technology Nodes
PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning
GAN-CTS: A Generative Adversarial Framework for Clock Tree Prediction and Optimization
Analysis and Optimization of Analog Design: Machine Learning Techniques in Analog Layout Automation
Layout Symmetry Annotation for Analog Circuits with Graph Neural Networks
ParaGraph: Layout parasitics and device parameter prediction using graph neural network
GCN-RL circuit designer: Transferable transistor sizing with graph neural networks and reinforcement learn
Parasitic-Aware Analog Circuit Sizing with Graph Neural Networks and Bayesian Optimization
Logic and Physical Verification: Deep Predictive Coverage Collection/ Dynamically Optimized Test Generation Using Machine Learning
Novelty-Driven Verification: Using Machine Learning to Identify Novel Stimuli and Close Coverage
Using Machine Learning Clustering To Find Large Coverage Holes
GAN-OPC: Mask optimization with lithography-guided generative adversarial nets
Layout hotspot detection with feature tensor generation and deep biased learning.

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