1 citations · 2 across the 8 of their papers we have counts for
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Beyond Flat Netlist: Hierarchical Graph Representation Learning for Scalable Analysis of Sequential Circuits
Jingyi Zhou, Zhengyuan Shi, Jiaying Zhu +2
Circuit Representation Learning (CRL) offers a powerful paradigm to guide and optimize core Electronic Design Automation (EDA) tasks, but its practical adoption is hindered by the…
DynamicRTL: RTL Representation Learning for Dynamic Circuit Behavior
Ruiyang Ma, Yunhao Zhou, Yipeng Wang +9
There is a growing body of work on using Graph Neural Networks (GNNs) to learn representations of circuits, focusing primarily on their static characteristics. However, these model…
Alignment Unlocks Complementarity: A Framework for Multiview Circuit Representation Learning
Zhengyuan Shi, Jingxin Wang, Wentao Jiang +5
Multiview learning on Boolean circuits holds immense promise, as different graph-based representations offer complementary structural and semantic information. However, the vast st…
DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning
Zhengyuan Shi, Chengyu Ma, Ziyang Zheng +7
We introduce DeepCell, a novel circuit representation learning framework that effectively integrates multiview information from both And-Inverter Graphs (AIGs) and Post-Mapping (PM…
DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scale
Ziyang Zheng, Shan Huang, Jianyuan Zhong +4
Circuit representation learning has become pivotal in electronic design automation, enabling critical tasks such as testability analysis, logic reasoning, power estimation, and SAT…
DeepGate3: Towards Scalable Circuit Representation Learning
Zhengyuan Shi, Ziyang Zheng, Sadaf Khan +3
Circuit representation learning has shown promising results in advancing the field of Electronic Design Automation (EDA). Existing models, such as DeepGate Family, primarily utiliz…