BoolSkeleton: Boolean Network Skeletonization via Homogeneous Pattern Reduction

• URL: http://arxiv.org/abs/2511.02196v1
• Date: Tue, 04 Nov 2025 02:25:29 GMT
• Title: BoolSkeleton: Boolean Network Skeletonization via Homogeneous Pattern Reduction
• Abstract summary: We introduce BoolSkeleton, a novel Boolean network skeletonization method.<n>It improves the consistency and reliability of design-specific evaluations.<n>These experiments underscore the potential of BoolSkeleton to enhance design consistency in logic synthesis.
• Score: 4.8942111496121266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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