{"ID":2846016,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.02196","arxiv_id":"2511.02196","title":"BoolSkeleton: Boolean Network Skeletonization via Homogeneous Pattern Reduction","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.","short_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 B...","url_abs":"https://arxiv.org/abs/2511.02196","url_pdf":"https://arxiv.org/pdf/2511.02196v1","authors":"[\"Liwei Ni\",\"Jiaxi Zhang\",\"Shenggen Zheng\",\"Junfeng Liu\",\"Xingyu Meng\",\"Biwei Xie\",\"Xingquan Li\",\"Huawei Li\"]","published":"2025-11-04T02:25:29Z","proceeding":"cs.AR","tasks":"[\"cs.AR\",\"cs.AI\"]","methods":"[\"LoRA\"]","has_code":false}