Area-oriented Boolean Resubstitution with Efficient Dependency Function Computation

Boolean resubstitution is a widely recognized and utilized optimization algorithm for logic networks. In this paper, we present an enhancement to the traditional resubstitution approach by integrating the Semi-Tensor Product (STP). Specifically, our novel method leverages STP to compute all feasible dependency functions for a target node in a single calculation during the resubstitution process. By utilizing these dependency functions, we can derive a more optimal implementation for the target node through decomposition and exact synthesis techniques, ultimately leading to more efficient logic network optimization. Experiments on the IWLS benchmark suite demonstrate that our method outperforms state-of-the-art (SOTA) methods. Specifically, our approach achieves an 7% improvement in the size optimization. In 6-LUT mapping, it achieves an 4.6% improvement in the size-depth product. Furthermore, applying our method after iterating the SOTA resubstitution algorithm until convergence further enhances optimization by 8.17%.