BoolSkeleton : Boolean Network Skeletonization via Homogeneous Pattern Reduction

2025-11-04

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

AI Summary

Boolean networks exhibiting functional equivalence often possess heterogeneous structures, impeding structural consistency assessment. Method: We propose BoolSkeleton, a framework that standardizes structurally diverse yet logically equivalent Boolean networks via isomorphism-pattern recognition and adjustable-granularity skeletonization. It constructs a Boolean dependency graph, annotates node functional states, defines homogeneous/heterogeneous patterns, and introduces a tunable parameter *K* to constrain fan-in size for granular control over reduction. Contribution/Results: BoolSkeleton preserves critical functional dependencies while significantly enhancing structural consistency and robustness. Experiments demonstrate an average >55% improvement in accuracy on temporal prediction tasks. Furthermore, its efficacy and generalizability are validated across downstream applications—including model compression, classification, and critical path analysis—confirming broad utility in Boolean network analysis and simplification.

Technology Category

• Machine Learning: Structured Learning
• Constraint Satisfaction and Optimization: Satisfiability
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• Boolean networks with identical functionality exhibit diverse graph structures
• BoolSkeleton improves consistency and reliability of design-specific evaluations
• It enables fine-tuned control over granularity of graph reduction

Innovation

• Transforms Boolean network into dependency graph
• Reduces homogeneous patterns while preserving heterogeneous ones
• Controls reduction granularity with parameter K