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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 31·5 min read
Beyond Flat Netlist: Hierarchical Graph Representation Learning for Scalable Analysis of Sequential Circuits
TL;DR
DeepSeq3 is a hierarchical framework for Circuit Representation Learning (CRL) that enhances the analysis of sequential circuits by modeling both local logic and global state transitions. It achieves an 18% reduction in bounded model checking solving time while ensuring correctness.
✦ Why It Matters
Consider adopting DeepSeq3 for your EDA tasks to improve circuit analysis efficiency today.
Key Takeaways
- Implement DeepSeq3 for scalable analysis of large netlists.
- Utilize the dual GNN architecture for improved circuit representation.
- Adopt the state-centric pre-training scheme for better temporal understanding.
- Leverage the 18% reduction in BMC solving time for efficiency.
How It Works
DeepSeq3 employs a hierarchical approach to circuit representation, dividing circuits into fine-grained subgraphs around flip-flops and a high-level Super-Node Graph. This structure allows the dual GNN architecture to learn both local Boolean logic and global state transitions effectively.
The state-centric pre-training scheme enhances the model's ability to predict state reachability, which is crucial for understanding the temporal dynamics of circuits.