Machine learning for electronic design automation: A survey
With the down-scaling of CMOS technology, the design complexity of very large-scale
integrated is increasing. Although the application of machine learning (ML) techniques in …
integrated is increasing. Although the application of machine learning (ML) techniques in …
MLCAD: A survey of research in machine learning for CAD keynote paper
Due to the increasing size of integrated circuits (ICs), their design and optimization phases
(ie, computer-aided design, CAD) grow increasingly complex. At design time, a large design …
(ie, computer-aided design, CAD) grow increasingly complex. At design time, a large design …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
A timing engine inspired graph neural network model for pre-routing slack prediction
Fast and accurate pre-routing timing prediction is essential for timing-driven placement since
repetitive routing and static timing analysis (STA) iterations are expensive and …
repetitive routing and static timing analysis (STA) iterations are expensive and …
A survey of machine learning for computer architecture and systems
It has been a long time that computer architecture and systems are optimized for efficient
execution of machine learning (ML) models. Now, it is time to reconsider the relationship …
execution of machine learning (ML) models. Now, it is time to reconsider the relationship …
A survey of graph neural networks for electronic design automation
Driven by Moore's law, the chip design complexity is steadily increasing. Electronic Design
Automation (EDA) has been able to cope with the challenging very large-scale integration …
Automation (EDA) has been able to cope with the challenging very large-scale integration …
Graph representation learning and its applications: a survey
Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …
representation learning is a significant task since it could facilitate various downstream …
Gratis: Deep learning graph representation with task-specific topology and multi-dimensional edge features
Graph is powerful for representing various types of real-world data. The topology (edges'
presence) and edges' features of a graph decides the message passing mechanism among …
presence) and edges' features of a graph decides the message passing mechanism among …
ALIGN: A system for automating analog layout
ALIGN: A System for Automating Analog Layout Page 1 8 2168-2356/20©2020 IEEE
Copublished by the IEEE CEDA, IEEE CASS, IEEE SSCS, and TTTC IEEE Design&Test …
Copublished by the IEEE CEDA, IEEE CASS, IEEE SSCS, and TTTC IEEE Design&Test …
A comprehensive survey on electronic design automation and graph neural networks: Theory and applications
Driven by Moore's law, the chip design complexity is steadily increasing. Electronic Design
Automation (EDA) has been able to cope with the challenging very large-scale integration …
Automation (EDA) has been able to cope with the challenging very large-scale integration …