BoolSkeleton: Boolean Network Skeletonization via Homogeneous Pattern Reduction
Liwei Ni, Jiaxi Zhang, Shenggen Zheng, Junfeng Liu, Xingyu Meng, Biwei Xie, Xingquan Li, Huawei Li
TL;DR
BoolSkeleton addresses the challenge of structural variability in Boolean networks arising from Boolean equivalence by proposing a two-phase skeletonization method that preserves critical functionality while coarsening the graph. The approach converts a Boolean network into a Boolean dependency graph in Phase 1, assigns node statuses, and then applies iterative, fanin-limited homogeneous pattern reductions in Phase 2 to obtain a skeleton that maintains reachability and topological order. Empirical evaluations across compression, classification, critical path analysis, and timing-prediction tasks demonstrate that BoolSkeleton can significantly improve downstream performance, notably achieving over 55% reduction in timing-prediction error on average compared to the original network. This work highlights the potential of functionally informed skeletonization to enhance design-consistency and efficiency in logic synthesis, with adaptable coarsening controlled by the parameter K and room for integration with partitioning-based approaches for large-scale circuits.
Abstract
Boolean equivalence allows Boolean networks with identical functionality to exhibit diverse graph structures. This gives more room for exploration in logic optimization, while also posing a challenge for tasks involving consistency between Boolean networks. To tackle this challenge, we introduce BoolSkeleton, a novel Boolean network skeletonization method that improves the consistency and reliability of design-specific evaluations. BoolSkeleton comprises two key steps: preprocessing and reduction. In preprocessing, the Boolean network is transformed into a defined Boolean dependency graph, where nodes are assigned the functionality-related status. Next, the homogeneous and heterogeneous patterns are defined for the node-level pattern reduction step. Heterogeneous patterns are preserved to maintain critical functionality-related dependencies, while homogeneous patterns can be reduced. Parameter K of the pattern further constrains the fanin size of these patterns, enabling fine-tuned control over the granularity of graph reduction. To validate BoolSkeleton's effectiveness, we conducted four analysis/downstream tasks around the Boolean network: compression analysis, classification, critical path analysis, and timing prediction, demonstrating its robustness across diverse scenarios. Furthermore, it improves above 55% in the average accuracy compared to the original Boolean network for the timing prediction task. These experiments underscore the potential of BoolSkeleton to enhance design consistency in logic synthesis.