BoolSkeleton: Boolean Network Skeletonization via Homogeneous Pattern Reduction

TL;DR

BoolSkeleton is a novel method for Boolean network skeletonization that enhances design consistency and reliability by reducing homogeneous patterns while preserving critical heterogeneous patterns, validated through multiple analysis tasks.

Contribution

Introduces BoolSkeleton, a new Boolean network reduction technique that improves consistency and accuracy in logic synthesis tasks through pattern-based graph reduction.

Findings

Achieves over 55% improvement in timing prediction accuracy.

Demonstrates robustness across compression, classification, and critical path analysis.

Effectively balances pattern reduction with functionality preservation.

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.