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 …
A review of graph neural network applications in mechanics-related domains
Mechanics-related tasks often present unique challenges in achieving accurate geometric
and physical representations, particularly for non-uniform structures. Graph neural networks …
and physical representations, particularly for non-uniform structures. Graph neural networks …
Versatile multi-stage graph neural network for circuit representation
Due to the rapid growth in the scale of circuits and the desire for knowledge transfer from old
designs to new ones, deep learning technologies have been widely exploited in Electronic …
designs to new ones, deep learning technologies have been widely exploited in Electronic …
Graph neural networks: A powerful and versatile tool for advancing design, reliability, and security of ICs
Graph neural networks (GNNs) have pushed the state-of-the-art (SOTA) for performance in
learning and predicting on large-scale data present in social networks, biology, etc. Since …
learning and predicting on large-scale data present in social networks, biology, etc. Since …
SyncTREE: fast timing analysis for integrated circuit design through a physics-informed tree-based graph neural network
Nowadays integrated circuits (ICs) are underpinning all major information technology
innovations including the current trends of artificial intelligence (AI). Modern IC designs often …
innovations including the current trends of artificial intelligence (AI). Modern IC designs often …
Benchmarking end-to-end performance of ai-based chip placement algorithms
Z Wang, Z Geng, Z Tu, J Wang, Y Qian, Z Xu… - ar** for coarse-grained reconfigurable architectures with reinforcement learning and monte-carlo tree search
Coarse-grained reconfigurable architecture (CGRA) has become a promising candidate for
data-intensive computing due to its flexibility and high energy efficiency. CGRA compilers …
data-intensive computing due to its flexibility and high energy efficiency. CGRA compilers …