Logic Synthesis
Cross-source consensus on Logic Synthesis from 1 sources and 4 claims.
1 sources · 4 claims
How it works
Highlighted claims
- Useful GNN embeddings for logic synthesis must preserve functional and Boolean structure rather than just local topology. — Graph Computation Meets Circuit Algebra: A Task-Aligned Analysis of Graph Neural Networks for Electronic Design Automation
- DeepGate learns AIG gate embeddings using simulated signal probabilities and separates functional from structural representations. — Graph Computation Meets Circuit Algebra: A Task-Aligned Analysis of Graph Neural Networks for Electronic Design Automation
- MGVGA uses masked gate modeling and Verilog-AIG alignment because generic graph masking can break circuit equivalence. — Graph Computation Meets Circuit Algebra: A Task-Aligned Analysis of Graph Neural Networks for Electronic Design Automation
- DeepSeq2 represents sequential circuits by disentangling structural, functional, and sequential-behavior embeddings. — Graph Computation Meets Circuit Algebra: A Task-Aligned Analysis of Graph Neural Networks for Electronic Design Automation