TGSyn: Transformer Guided Topology-based Exact Synthesis for Majority-Inverter Graphs

Abstract

Exact synthesis is a method to find an optimal circuit that meets the specification. SAT-based exact synthesis is commonly used for its ability to allow for a more versatile approach to addressing synthesis requirements. However, the runtime of SAT-based exact synthesis is very long and unpredictable to check all possible structures. Recently, topology-based exact synthesis methods have been studied to reduce the runtime of exact synthesis, where all structures are classified into sub-classes of topologies and each sub-class is checked using a simpler Conjunctive Normal Form separately. There is a freedom on the order of topologies to be checked, and we found that the Transformer is effective to decide the order. This paper proposes a Transformer-guided topology-based exact synthesis method (TGSyn) to achieve better time efficiency. A Transformer model is used to predict the success probability of synthesis for each topology in topology-based exact synthesis, and the order is decided based on the predicted probability to accelerate the synthesis process. The proposed Transformer-based model achieves 98.56% of top-15 categorical accuracy. To evaluate the TGSyn, we used subgraphs in MIG with 3 to 5 inputs in EPFL and ISCAS’85 benchmarks using cut enumeration. The TGSyn reduces the runtime of the exact synthesis of 19,148 circuits by 64.02% compared with a method without Transformer guiding.