5 papers
TRACE: Learning to Compute on Circuit Graphs
Ziyang Zheng, Jiaying Zhu, Jingyi Zhou +1
Learning to compute, the ability to model the functional behavior of a circuit graph, is a fundamental challenge for graph representation learning. Yet, the dominant paradigm is ar…
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…