SMART: Graph Learning-Boosted Subcircuit Matching for Large-Scale Analog Circuits

Abstract

Subcircuit matching in a large-scale analog circuit is a fundamental problem in VLSI computer-aided design (CAD). Existing approaches suffer from a poor scalability issue for a large-scale analog circuit. In this article, we propose a graph learning-boosted subcircuit matching framework for large-scale analog circuits named SMART, consisting of two stages. In the first stage, we customize hypergraph neural networks to map circuit topology for embedding space. Then, coarse subcircuit recognition is directly performed in the embedding space by geometric relations between the query circuit and all candidate subcircuits within the target circuit. In the second stage, a radial matching method, including device attribute matching, connection relationship matching and uniqueness-based matching, is customized to perform fine matching and obtain matches between interconnections and devices in the query circuit and candidate subcircuits. Experimental results show our SMART can outperform state-of-the-art search-based method VF3 and learning-based method NeuroMatch, and achieve the fastest speed. Specifically, using our framework for subcircuit matching can achieve up to $135\times $ speedup with slight accuracy loss, and up to $7\times $ speedup while maintaining 100% accuracy.