ReVEAL: GNN-Guided Reverse Engineering for Formal Verification of Optimized Multipliers
This addresses verification challenges for hardware designers, but it is incremental as it builds on existing algebraic circuit verification techniques.
The paper tackles the problem of verifying optimized multiplier circuits by introducing ReVEAL, a graph-learning-based method for reverse engineering architectures, which improves scalability and accuracy compared to traditional rule-based approaches.
We present ReVEAL, a graph-learning-based method for reverse engineering of multiplier architectures to improve algebraic circuit verification techniques. Our framework leverages structural graph features and learning-driven inference to identify architecture patterns at scale, enabling robust handling of large optimized multipliers. We demonstrate applicability across diverse multiplier benchmarks and show improvements in scalability and accuracy compared to traditional rule-based approaches. The method integrates smoothly with existing verification flows and supports downstream algebraic proof strategies.